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CN115265581B - Calibration parameter determining method of laser radar and inertial measurement unit and related equipment - Google Patents

Calibration parameter determining method of laser radar and inertial measurement unit and related equipment Download PDF

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Publication number
CN115265581B
CN115265581B CN202210551578.0A CN202210551578A CN115265581B CN 115265581 B CN115265581 B CN 115265581B CN 202210551578 A CN202210551578 A CN 202210551578A CN 115265581 B CN115265581 B CN 115265581B
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target acquisition
laser radar
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acquisition time
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CN115265581A (en
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陈连胜
李秦
韩旭
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Guangzhou Weride Technology Co Ltd
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Guangzhou Weride Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/497—Means for monitoring or calibrating
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Manufacturing & Machinery (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

The invention relates to the technical field of automatic driving, and discloses a calibration parameter determining method and related equipment for a laser radar and an inertial measurement unit, which are used for widening the available range of calibration parameter determination between the laser radar and the inertial measurement unit. The calibration parameter determining method of the laser radar and the inertia measuring unit comprises the following steps: determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar; acquiring second observation data corresponding to each target acquisition moment through a preset vehicle inertia measurement unit, wherein the second observation data corresponds to the first observation data one by one; and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each target acquisition time to obtain the optimal calibration parameters.

Description

Calibration parameter determining method of laser radar and inertial measurement unit and related equipment
Technical Field
The invention relates to the technical field of automatic driving, in particular to a calibration parameter determining method of a laser radar and an inertial measurement unit and related equipment.
Background
Along with the development of automatic driving technology, multi-sensor feature fusion becomes an important technical means for improving environment perception capability, and because of the difference of the installation positions and the installation postures of different sensors, calibration parameters among the sensors are accurately determined, so that the method is a premise and a foundation for ensuring the feature fusion accuracy of the sensors.
For calibration parameters between the laser radar and the inertial measurement unit (Inertial Measurement Unit, IMU), the prior art generally needs to be calibrated based on a global positioning system (Global Positioning System, GPS), and this mode cannot be used in a scene with weak GPS signals, so that a certain limitation exists in determining the calibration parameters between the laser radar and the inertial measurement unit.
Disclosure of Invention
The invention provides a method and related equipment for determining calibration parameters of a laser radar and an inertial measurement unit, which are used for widening the available range of the calibration parameter determination between the laser radar and the inertial measurement unit.
The first aspect of the invention provides a calibration parameter determining method of a laser radar and an inertial measurement unit, comprising the following steps:
determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
Acquiring second observation data corresponding to each target acquisition time through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each target acquisition time to obtain the optimal calibration parameters.
Optionally, the determining, by using a multi-frame laser radar point cloud acquired by the laser radar, first observation data corresponding to each of the plurality of target acquisition moments, where the first observation data includes at least one of a first observation acceleration, a first observation rotational angular velocity, and a first observation pose, includes:
Acquiring multi-frame laser radar point clouds acquired by a laser radar, and performing odometer estimation on the multi-frame laser radar point clouds through a preset normal distribution transformation algorithm to obtain laser radar pose information respectively corresponding to a plurality of target acquisition moments;
And determining first observation data corresponding to each target acquisition time according to the laser radar pose information corresponding to each target acquisition time, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose.
Optionally, the determining, according to the laser radar pose information corresponding to each target acquisition time, first observation data corresponding to each target acquisition time, where the first observation data includes at least one of a first observation acceleration, a first observation rotation angular velocity, and a first observation pose includes:
Determining the pose information of the laser radar corresponding to each target acquisition moment as a first original pose corresponding to the target acquisition moment;
Performing curve fitting on the laser radar pose information corresponding to each target acquisition moment through a preset B spline algorithm to obtain a target track, and deriving the target track to obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment;
Based on external parameters between the laser radar and a preset vehicle inertia measurement unit, respectively converting a first original pose, a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment into a vehicle inertia measurement unit coordinate system to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition moment;
and determining the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time.
Optionally, the acquiring, by a preset vehicle inertia measurement unit, the second observation data corresponding to each target acquisition time includes:
Acquiring original measurement data in a vehicle stationary period through a preset vehicle inertia measurement unit, and carrying out average value calculation on the original measurement data to obtain gyroscope zero-bias data, wherein the gyroscope zero-bias data comprises acceleration zero-bias data and rotation angular velocity zero-bias data;
And determining second observation data of the vehicle inertia measurement unit at each target acquisition moment based on the gyroscope zero offset data.
Optionally, the performing iterative optimization solution of the calibration parameter to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each target acquisition time to obtain the optimal calibration parameter includes:
Performing residual calculation on each first observation data and corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between the first observation acceleration and the second observation acceleration, a second residual between the first observation rotation angular velocity and the second observation rotation angular velocity, and a third residual between the first observation pose and the second observation pose;
and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function based on the target residual error items corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit.
Optionally, when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observed pose and the second observed pose, the preset objective function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error representing acquisition time of kth frame laser radar point cloud, epsilon a representingIs represented by ε w Is represented by ε L Is a covariance of (c).
Optionally, the first residual, the second residual and the third residual are respectively:
wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of lidar point cloud, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of lidar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the lidar and vehicle inertial measurement unit, Representing a second relative pose between the target acquisition time of the kth frame of lidar point cloud and the target acquisition time of the k +1 frame of lidar point cloud,And representing a first relative pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
The second aspect of the present invention provides a calibration parameter determining apparatus for a lidar and an inertial measurement unit, comprising:
The system comprises a determining module, a target acquisition module and a target acquisition module, wherein the determining module is used for determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
the acquisition module is used for acquiring second observation data corresponding to each target acquisition moment through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
And the solving module is used for carrying out iterative optimization solving on the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each objective acquisition time to obtain the optimal calibration parameters.
Optionally, the determining module includes:
The estimating unit is used for acquiring multi-frame laser radar point clouds acquired by the laser radar, and performing odometer estimation on the multi-frame laser radar point clouds through a preset normal distribution transformation algorithm to obtain laser radar pose information corresponding to a plurality of target acquisition moments respectively;
the determining unit is used for determining first observation data corresponding to each target acquisition time according to the laser radar pose information corresponding to each target acquisition time, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose.
Optionally, the determining unit is specifically configured to:
Determining the pose information of the laser radar corresponding to each target acquisition moment as a first original pose corresponding to the target acquisition moment;
Performing curve fitting on the laser radar pose information corresponding to each target acquisition moment through a preset B spline algorithm to obtain a target track, and deriving the target track to obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment;
Based on external parameters between the laser radar and a preset vehicle inertia measurement unit, respectively converting a first original pose, a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment into a vehicle inertia measurement unit coordinate system to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition moment;
and determining the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time.
Optionally, the acquiring module is specifically configured to:
Acquiring original measurement data in a vehicle stationary period through a preset vehicle inertia measurement unit, and carrying out average value calculation on the original measurement data to obtain gyroscope zero-bias data, wherein the gyroscope zero-bias data comprises acceleration zero-bias data and rotation angular velocity zero-bias data;
And determining second observation data of the vehicle inertia measurement unit at each target acquisition moment based on the gyroscope zero offset data.
Optionally, the solving module is specifically configured to:
Performing residual calculation on each first observation data and corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between the first observation acceleration and the second observation acceleration, a second residual between the first observation rotation angular velocity and the second observation rotation angular velocity, and a third residual between the first observation pose and the second observation pose;
and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function based on the target residual error items corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit.
Optionally, when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observed pose and the second observed pose, the preset objective function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error representing acquisition time of kth frame laser radar point cloud, epsilon a representingIs represented by ε w Is represented by ε L Is a covariance of (c).
Optionally, the first residual, the second residual and the third residual are respectively:
wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of lidar point cloud, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of lidar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the lidar and vehicle inertial measurement unit, Representing a second relative pose between the target acquisition time of the kth frame of lidar point cloud and the target acquisition time of the k +1 frame of lidar point cloud,And representing a first relative pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
A third aspect of the present invention provides a calibration parameter determining apparatus of a lidar and an inertial measurement unit, comprising: a memory and at least one processor, the memory having a computer program stored therein; the at least one processor invokes the computer program in the memory to cause the calibration parameter determination device of the lidar and inertial measurement unit to perform the calibration parameter determination method of the lidar and inertial measurement unit described above.
A fourth aspect of the present invention provides a computer readable storage medium having a computer program stored therein, which when run on a computer causes the computer to perform the above-described method of determining calibration parameters of a lidar and an inertial measurement unit.
According to the technical scheme provided by the invention, first observation data respectively corresponding to a plurality of target acquisition moments are determined through multi-frame laser radar point clouds acquired by the laser radar, the first observation data comprise at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds; acquiring second observation data corresponding to each target acquisition time through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one; and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each target acquisition time to obtain the optimal calibration parameters. In the embodiment of the invention, the first observation data of the laser radar at the acquisition time of each frame of laser radar point cloud is acquired, and the one-to-one constraint relation is established with the second observation data determined by the IMU, so that the iterative optimization solution of the calibration parameters to be optimized can be carried out on the preset objective function through the constraint relation between the first observation data and the second observation data, thereby determining the optimal calibration parameters meeting the constraint relation, improving the accuracy of the calibration parameter determination, avoiding depending on GPS, and widening the available range of the calibration parameter determination between the laser radar and the inertial measurement unit.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a calibration parameter determining method of a laser radar and an inertial measurement unit according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a calibration parameter determining method of a laser radar and an inertial measurement unit according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a calibration parameter determining apparatus of a laser radar and inertial measurement unit according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another embodiment of a calibration parameter determining apparatus of a lidar and inertial measurement unit according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a calibration parameter determining apparatus of a lidar and inertial measurement unit according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a calibration parameter determining method of a laser radar and an inertial measurement unit and related equipment, which are used for widening the available range of calibration parameter determination between the laser radar and the inertial measurement unit.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
It can be understood that the execution body of the invention can be a calibration parameter determining device of the laser radar and the inertial measurement unit, and can also be a terminal or a server, and the invention is not limited in particular. The embodiment of the invention is described by taking the terminal as an execution main body as an example.
For ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and an embodiment of a method for determining calibration parameters of a lidar and an inertial measurement unit according to the embodiment of the present invention includes:
101. determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
It should be noted that, the multi-frame laser radar point cloud acquired through the preset laser radar is point cloud data in the vehicle motion state, and each frame of the acquisition time of the laser radar point cloud corresponds to one first observation data, where the first observation data includes at least one of a first observation acceleration, a first observation rotation angular velocity and a first observation pose, the first observation acceleration is used to indicate the vehicle acceleration corresponding to the acquisition time, the second observation rotation angular velocity is used to indicate the vehicle rotation angular velocity (i.e. angular velocity) corresponding to the acquisition time, and the third observation pose is used to indicate the vehicle pose corresponding to the acquisition time. The first observation data are used for forming a constraint relation with the second observation data determined by the subsequent IMU, so that iterative optimization solution of the calibration parameters to be optimized is performed, the accuracy of determining the calibration parameters of the laser radar and the IMU is improved, the GPS is not required, and the application scene is widened.
In order to avoid the mutual influence of the first observation data and the second observation data of the IMU, the determining process of the first observation data does not involve data acquired by the IMU, in one embodiment, the terminal performs laser radar odometer estimation on a multi-frame laser radar point cloud through a preset laser radar odometer estimation algorithm to obtain a first original pose corresponding to each target acquisition time, converts the first original pose corresponding to each target acquisition time to an IMU coordinate system according to external parameters between the laser radar and the IMU to obtain a first observation pose corresponding to each target acquisition time, performs spline curve generation on the first observation pose corresponding to each target acquisition time to obtain a target spline curve, finally, the terminal derives the target spline curve to obtain a first original acceleration and a first original velocity corresponding to each target acquisition time, converts the first original acceleration and the first original rotation angle velocity corresponding to each target acquisition time to the IMU coordinate system according to external parameters between the laser radar and the IMU to obtain the first observation acceleration and the first rotation angle corresponding to each target acquisition time, and the first observation angle data is used for indicating the first observation angle coordinate system of the laser radar. According to the method and the device, parameters such as acceleration, rotation angular velocity and pose observed by the laser radar can be accurately determined, so that accuracy of determining calibration parameters of the laser radar and the IMU is improved.
It will be appreciated that the target acquisition time is used to indicate the acquisition time of a frame of lidar point cloud, for example, the lidar acquires 100 frames of lidar point cloud, where each frame corresponds to one acquisition time, and then all the target acquisition times also include the acquisition time of 100 frames of lidar point cloud.
102. Acquiring second observation data corresponding to each target acquisition moment through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
The vehicle inertia measurement unit is a device for measuring acceleration and three-axis attitude angle (i.e., angular velocity) of an object by an accelerometer and a gyroscope, and is installed in a vehicle in advance for measuring acceleration and rotational angular velocity of the vehicle, so that in one embodiment, the terminal acquires a second observed acceleration and a second observed rotational angular velocity corresponding to each target acquisition time by a preset vehicle inertia vehicle unit. For the observation pose of the IMU, because the external parameter (target external parameter) between the lidar and the IMU is one of the calibration parameters to be optimized, specifically, the terminal performs lidar odometer estimation on the multi-frame lidar point cloud to obtain a second original pose corresponding to each target acquisition moment, performs curve fitting on the second original pose corresponding to each target acquisition moment through a preset B-spline algorithm to obtain a second track, and performs interpolation or weighted average on the second track to obtain a second observation pose corresponding to each target acquisition moment. The method and the device have the advantages that constraint relations are formed through the observation data in one-to-one correspondence, so that iterative optimization solving of the calibration parameters to be optimized is carried out, accuracy of determining the calibration parameters of the laser radar and the IMU is improved, the GPS is not needed, and application scenes are widened. And finally, determining second observation data corresponding to each target acquisition time according to the second observation pose, the second observation rotation angular velocity and the second observation acceleration corresponding to each target acquisition time.
In one embodiment, the second observation data further includes parameters such as a second observation speed and a second observation direction, which can be determined by the IMU, the second observation data and the first observation data are in a one-to-one correspondence, each set of corresponding observation data combination is used for forming a constraint relationship, a solution condition is provided for iterative optimization solution of a calibration parameter to be optimized subsequently, specifically, the first observation acceleration and the corresponding second observation acceleration form an observation data combination, the first observation rotation angular velocity and the corresponding second observation rotation angular velocity form an observation data combination, the first observation pose and the corresponding second observation pose form an observation data combination, and the like, and the method is applicable to other first observation data and second observation data, and is not repeated in detail.
103. And carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each target acquisition time to obtain the optimal calibration parameters.
In this embodiment, based on each target acquisition time, nonlinear least square calculation is performed on each first observation data and the corresponding second observation data through a preset target function, so as to perform iterative optimization solution of the calibration parameters to be optimized through the preset target function, and obtain the optimal calibration parameters. It should be noted that, the nonlinear least square objective function, that is, the preset objective function has a definite physical meaning—a residual error, so when the residual error between the first observation data and the second observation data is minimum, the preset objective function is used to determine the value of the calibration parameter to be optimized, that is, the optimal solution of the preset objective function is the optimal calibration parameter.
In one embodiment, the calibration parameters to be optimized include, but are not limited to, target external parameters, time offset values between the laser radar and the IMU, gyroscope zero offset data of the IMU and other data used for calibrating and calibrating the laser radar and the IMU, and the optimal calibration parameters are used for indicating the value of the calibration parameters to be optimized when the residual error between the first observation data and the second observation data is minimum. According to the method and the device, the constraint relation can be formed on the data observed by different sensors, so that nonlinear least square value solving can be carried out, and therefore the optimal solution can be accurately obtained, the accuracy of determining the calibration parameters between the laser radar and the IMU is improved, the GPS is not required to be relied on, and the application scene is widened.
In the embodiment of the invention, the first observation data of the laser radar at the acquisition time of each frame of laser radar point cloud is acquired, and the one-to-one constraint relation is established with the second observation data determined by the IMU, so that the iterative optimization solution of the calibration parameters to be optimized can be carried out on the preset objective function through the constraint relation between the first observation data and the second observation data, thereby determining the optimal calibration parameters meeting the constraint relation, improving the accuracy of the calibration parameter determination, avoiding depending on GPS, and widening the available range of the calibration parameter determination between the laser radar and the inertial measurement unit.
Referring to fig. 2, another embodiment of a calibration parameter determining method of a lidar and an inertial measurement unit according to an embodiment of the present invention includes:
201. Determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
specifically, step 201 includes: acquiring multi-frame laser radar point clouds acquired by a laser radar, and performing odometer estimation on the multi-frame laser radar point clouds through a preset normal distribution transformation algorithm to obtain laser radar pose information respectively corresponding to a plurality of target acquisition moments; and determining first observation data corresponding to each target acquisition time according to the laser radar pose information corresponding to each target acquisition time, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose.
In this embodiment, in order to accurately register each frame of laser radar point cloud and ensure real-time performance of registration, a normal distribution transformation algorithm is adopted to register multiple frames of laser radar point clouds, so as to obtain laser radar pose information corresponding to each target acquisition time, where the laser radar pose information is used to indicate pose information observed by a laser radar, and is used to determine first observation data corresponding to each target acquisition time. According to the method and the device, the odometer estimation can be rapidly and accurately carried out through a normal distribution transformation algorithm, so that accurate pose information of the laser radar is obtained, and the accuracy of determining the calibration parameters of the laser radar and the IMU is improved.
Further, determining first observation data corresponding to each target acquisition time according to laser radar pose information corresponding to each target acquisition time, wherein the first observation data comprises at least one of a first observation acceleration, a first observation rotation angular velocity and a first observation pose, and the method comprises the following steps: determining the pose information of the laser radar corresponding to each target acquisition moment as a first original pose corresponding to the target acquisition moment; performing curve fitting on the laser radar pose information corresponding to each target acquisition moment through a preset B spline algorithm to obtain a target track, and deriving the target track to obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment; based on external parameters between the laser radar and a preset vehicle inertia measurement unit, respectively converting a coordinate system of the vehicle inertia measurement unit for a first original pose, a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition moment; and determining the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time.
In this embodiment, since the first observation pose is used to indicate pose information observed by the lidar, the odometer estimation is performed on the multi-frame lidar point cloud by using a preset normal distribution transformation algorithm, and the obtained pose information of the lidar corresponding to each of the multiple target acquisition moments can be directly determined as the first original pose corresponding to the target acquisition moment, so as to obtain the first original pose corresponding to each of the target acquisition moments. And for the acceleration and the rotation angular velocity, spline curve fitting is carried out on the laser radar pose information corresponding to each target acquisition time through a preset B spline (B-spline) algorithm, so that a smooth target track is obtained, the target track comprises pose points corresponding to each target acquisition time, and the terminal can obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition time by deriving the pose points corresponding to each target acquisition time in the target track. The terminal converts the first original pose, the first original acceleration and the first original rotation angular velocity corresponding to each target acquisition time into the IMU coordinate system based on external parameters between the laser radar and the IMU to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition time, and finally, the terminal determines the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time. According to the method and the device, the laser radar can accurately obtain the pose, the acceleration and the rotation angular velocity observed by the laser radar without depending on the IMU, and the determination accuracy of calibration parameters between the laser radar and the IMU is improved.
202. Acquiring second observation data corresponding to each target acquisition moment through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
Specifically, step 202 includes: acquiring original measurement data in a vehicle stationary period through a preset vehicle inertia measurement unit, and carrying out average value calculation on the original measurement data to obtain gyroscope zero-bias data, wherein the gyroscope zero-bias data comprises acceleration zero-bias data and rotation angular velocity zero-bias data; and determining second observation data of the vehicle inertia measurement unit at each target acquisition moment based on the gyroscope zero offset data.
It should be noted that, during automatic driving, when the gyroscope in the IMU is stationary, the gyroscope still generates Bias, so as to generate gyroscope zero Bias data (Bias), the gyroscope zero Bias data includes acceleration zero Bias data and rotation angular velocity zero Bias data, and the terminal calculates the average value of the original measurement data output by the gyroscope of the IMU in the stationary period of the vehicle, so as to obtain the gyroscope zero Bias data, wherein the original measurement data is a continuous signal curve, and the average value of the curve is the gyroscope zero Bias data. The terminal obtains a second observation acceleration corresponding to each target acquisition time by combining acceleration zero offset data in the gyroscope zero offset data with acceleration acquired by the IMU at each target acquisition time, and obtains a second observation rotation angular velocity corresponding to each target acquisition time by combining rotation angular velocity zero offset data in the gyroscope zero offset data with rotation angular velocity acquired by the IMU at each target acquisition time, wherein the second observation pose corresponding to each target acquisition time is the same as that in step 102, and is not repeated here. And finally, the terminal combines the second observation pose, the second observation acceleration and the second observation rotation angular velocity corresponding to each target acquisition moment to obtain second observation data corresponding to each target acquisition moment. According to the method and the device, zero offset data of the gyroscope can be dynamically adjusted in each power-on process, so that calibration parameters of the laser radar and the IMU can be dynamically adjusted in an automatic driving process, and accuracy of automatic driving control is improved.
203. Performing residual calculation on each first observation data and corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between a first observation acceleration and a second observation acceleration, a second residual between a first observation rotation angular velocity and a second observation rotation angular velocity, and a third residual between a first observation pose and a second observation pose;
Wherein, the first residual error, the second residual error and the third residual error are respectively:
wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of lidar point cloud, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of lidar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the lidar and vehicle inertial measurement unit, Representing a second relative pose between the target acquisition time of the kth frame of lidar point cloud and the target acquisition time of the k +1 frame of lidar point cloud,And representing a first relative pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
In this embodiment, since the first observation data includes at least one of the first observation acceleration, the first observation rotational angular velocity, and the first observation pose, the second observation data includes at least one of the second observation acceleration, the second observation rotational angular velocity, and the second observation data and the first observation data are in one-to-one correspondence, the content of the target residual item depends on the content of the first observation data and the second observation data, that is, if the first observation data includes only the first observation acceleration, the second observation data also includes only the second observation acceleration, then the target residual item includes only the first residual between the first observation acceleration and the second observation acceleration, and if the first observation data includes the first observation acceleration and the first observation rotational angular velocity, then the second observation acceleration and the second observation rotational angular velocity, then the target residual item includes the first residual between the first observation acceleration and the second observation acceleration, the second residual between the first observation rotational angular velocity, and the second observation rotational angular velocity, and so on, and detailed description is omitted.
In one embodiment, the first residual term is used to indicate the difference between the first observed acceleration, the second observed acceleration, and the angular velocity zero offset data, the second residual term is used to indicate the difference between the second observed rotational angular velocity, and the rotational angular velocity zero offset data, and the third residual term is used to indicate the difference between the relative pose of the adjacent frame in the first observed pose and the relative pose of the adjacent frame in the second observed pose corresponding to all target acquisition moments. According to the method and the device, residual calculation can be carried out by combining zero offset data of a gyroscope, so that iterative optimization solving of calibration parameters to be optimized is more accurate, and the calibration parameters of the laser radar and the IMU are more accurately determined.
204. And carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function based on the target residual error items corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit.
It will be appreciated that since the content of the target residual term depends on the content of the first and second observations, the preset objective function also varies with the number of terms of the target residual term, in one embodiment, when the target residual term includes only the first residual between the first and second observations accelerations, the preset objective function is:
when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration and a third residual between the first observed pose and the second observed pose, the preset objective function is:
Similarly, when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observation pose and the second observation pose, the preset target function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error showing acquisition time of kth frame laser radar point cloud, epsilon a representsIs represented by ε w Is represented by ε L Is a covariance of (c).
It will be appreciated that argmin (f (x)) is used to indicate the variable value at which the objective function f (x) takes its minimum, i.eAnd taking the value of x as the optimal calibration parameter when the minimum value is taken. In addition, in the case of the optical fiber,AndThe Markov distance is represented instead of the Euclidean distance, so that the accuracy of iterative optimization solution of the calibration parameters to be optimized is improved through the matrix norm of covariance, and the accuracy of determination of the laser radar and the IMU calibration parameters is improved.
In the embodiment of the invention, the first observation data of the laser radar at the acquisition time of each frame of laser radar point cloud is acquired, and the one-to-one constraint relation is established with the second observation data determined by the IMU, so that the iterative optimization solution of the calibration parameters to be optimized can be carried out on the preset nonlinear least square value solving function through the constraint relation between the first observation data and the second observation data, thereby determining the optimal calibration parameters meeting the constraint relation, improving the accuracy of the determination of the calibration parameters, avoiding depending on GPS, and widening the available range of the determination of the calibration parameters between the laser radar and the inertial measurement unit.
The method for determining calibration parameters of the lidar and the inertial measurement unit in the embodiment of the present invention is described above, and the device for determining calibration parameters of the lidar and the inertial measurement unit in the embodiment of the present invention is described below, referring to fig. 3, one embodiment of the device for determining calibration parameters of the lidar and the inertial measurement unit in the embodiment of the present invention includes:
the determining module 301 is configured to determine first observation data respectively corresponding to multiple target acquisition moments through multiple frames of laser radar point clouds acquired by a laser radar, where the first observation data includes at least one of a first observation acceleration, a first observation rotation angular velocity, and a first observation pose, and the target acquisition moments are used to indicate an acquisition moment of each frame of laser radar point clouds;
The acquiring module 302 is configured to acquire second observation data corresponding to each target acquisition time through a preset vehicle inertia measurement unit, where the second observation data includes at least one of a second observation acceleration, a second observation rotation angular velocity, and a second observation pose, and the second observation data corresponds to the first observation data one to one;
and the solving module 303 is configured to perform iterative optimization solving of the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each objective acquisition time, so as to obtain optimal calibration parameters.
In the embodiment of the invention, the first observation data of the laser radar at the acquisition time of each frame of laser radar point cloud is acquired, and the one-to-one constraint relation is established with the second observation data determined by the IMU, so that the iterative optimization solution of the calibration parameters to be optimized can be carried out on the preset objective function through the constraint relation between the first observation data and the second observation data, thereby determining the optimal calibration parameters meeting the constraint relation, improving the accuracy of the calibration parameter determination, avoiding depending on GPS, and widening the available range of the calibration parameter determination between the laser radar and the inertial measurement unit.
Referring to fig. 4, another embodiment of the calibration parameter determining apparatus for a lidar and an inertial measurement unit according to an embodiment of the present invention includes:
the determining module 301 is configured to determine first observation data respectively corresponding to multiple target acquisition moments through multiple frames of laser radar point clouds acquired by a laser radar, where the first observation data includes at least one of a first observation acceleration, a first observation rotation angular velocity, and a first observation pose, and the target acquisition moments are used to indicate an acquisition moment of each frame of laser radar point clouds;
The acquiring module 302 is configured to acquire second observation data corresponding to each target acquisition time through a preset vehicle inertia measurement unit, where the second observation data includes at least one of a second observation acceleration, a second observation rotation angular velocity, and a second observation pose, and the second observation data corresponds to the first observation data one to one;
and the solving module 303 is configured to perform iterative optimization solving of the calibration parameters to be optimized on the preset objective function through the first observation data and the second observation data corresponding to each objective acquisition time, so as to obtain optimal calibration parameters.
Optionally, the determining module 301 includes:
The estimating unit 3011 is configured to obtain multiple frames of laser radar point clouds acquired by the laser radar, and perform odometer estimation on the multiple frames of laser radar point clouds through a preset normal distribution transformation algorithm, so as to obtain laser radar pose information corresponding to multiple target acquisition moments respectively;
A determining unit 3012, configured to determine first observation data corresponding to each target acquisition time according to laser radar pose information corresponding to each target acquisition time, where the first observation data includes at least one of a first observation acceleration, a first observation rotational angular velocity, and a first observation pose.
Optionally, the determining unit 3012 is specifically configured to:
Determining the pose information of the laser radar corresponding to each target acquisition moment as a first original pose corresponding to the target acquisition moment;
Performing curve fitting on the laser radar pose information corresponding to each target acquisition moment through a preset B spline algorithm to obtain a target track, and deriving the target track to obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment;
Based on external parameters between the laser radar and a preset vehicle inertia measurement unit, respectively converting a first original pose, a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment into a vehicle inertia measurement unit coordinate system to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition moment;
and determining the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time.
Optionally, the acquiring module 302 is specifically configured to:
Acquiring original measurement data in a vehicle stationary period through a preset vehicle inertia measurement unit, and carrying out average value calculation on the original measurement data to obtain gyroscope zero-bias data, wherein the gyroscope zero-bias data comprises acceleration zero-bias data and rotation angular velocity zero-bias data;
And determining second observation data of the vehicle inertia measurement unit at each target acquisition moment based on the gyroscope zero offset data.
Optionally, the solving module 303 is specifically configured to:
Performing residual calculation on each first observation data and corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between the first observation acceleration and the second observation acceleration, a second residual between the first observation rotation angular velocity and the second observation rotation angular velocity, and a third residual between the first observation pose and the second observation pose;
and carrying out iterative optimization solution on the calibration parameters to be optimized on the preset objective function based on the target residual error items corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit.
Optionally, when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observed pose and the second observed pose, the preset objective function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error representing acquisition time of kth frame laser radar point cloud, epsilon a representingIs represented by ε w Is represented by ε L Is a covariance of (c).
Optionally, the first residual, the second residual and the third residual are respectively:
wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of lidar point cloud, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of lidar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of lidar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the lidar and vehicle inertial measurement unit, Representing a second relative pose between the target acquisition time of the kth frame of lidar point cloud and the target acquisition time of the k +1 frame of lidar point cloud,And representing a first relative pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
In the embodiment of the invention, the first observation data of the laser radar at the acquisition time of each frame of laser radar point cloud is acquired, and the one-to-one constraint relation is established with the second observation data determined by the IMU, so that the iterative optimization solution of the calibration parameters to be optimized can be carried out on the preset nonlinear least square value solving function through the constraint relation between the first observation data and the second observation data, thereby determining the optimal calibration parameters meeting the constraint relation, improving the accuracy of the determination of the calibration parameters, avoiding depending on GPS, and widening the available range of the determination of the calibration parameters between the laser radar and the inertial measurement unit.
The calibration parameter determining device of the lidar and the inertial measurement unit in the embodiment of the present invention is described in detail above from the point of view of modularized functional entities in fig. 3 and fig. 4, and the calibration parameter determining device of the lidar and the inertial measurement unit in the embodiment of the present invention is described in detail below from the point of view of hardware processing.
Fig. 5 is a schematic structural diagram of a calibration parameter determining apparatus for a laser radar and an inertial measurement unit according to an embodiment of the present invention, where the calibration parameter determining apparatus 500 for a laser radar and an inertial measurement unit may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (e.g., one or more processors) and a memory 520, and one or more storage mediums 530 (e.g., one or more mass storage devices) storing application programs 533 or data 532. Wherein memory 520 and storage medium 530 may be transitory or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of computer program operations in the calibration parameter determination device 500 for a lidar and inertial measurement unit. Still further, the processor 510 may be arranged to communicate with the storage medium 530, executing a series of computer program operations in the storage medium 530 on the calibration parameter determination device 500 of the lidar and inertial measurement unit.
The calibration parameter determination device 500 of the lidar and inertial measurement unit may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input/output interfaces 560, and/or one or more operating systems 531, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the configuration of the calibration parameter determination device of the lidar and inertial measurement unit shown in fig. 5 does not constitute a limitation of the calibration parameter determination device of the lidar and inertial measurement unit, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The invention also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer readable computer program, and the computer readable computer program when executed by the processor causes the processor to execute the steps of the calibration parameter determining method of the laser radar and the inertia measuring unit in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, in which a computer program is stored, which when run on a computer causes the computer to perform the steps of the calibration parameter determination method of the lidar and the inertial measurement unit.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising a number of computer programs for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. The method for determining the calibration parameters of the laser radar and the inertial measurement unit is characterized by comprising the following steps of:
determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
Acquiring second observation data corresponding to each target acquisition time through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
Performing residual calculation on each first observation data and corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between the first observation acceleration and the second observation acceleration, a second residual between the first observation rotation angular velocity and the second observation rotation angular velocity, and a third residual between the first observation pose and the second observation pose;
performing iterative optimization solution of calibration parameters to be optimized on a preset objective function based on a target residual error item corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit;
when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observation pose and the second observation pose, the preset target function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error representing acquisition time of kth frame laser radar point cloud, epsilon a representingIs represented by ε w Is represented by ε L Is a covariance of (2);
the first residual, the second residual and the third residual are respectively:
Wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of vehicle inertia measurement unit, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of laser radar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of vehicle inertia measurement unit, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of laser radar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the laser radar and the vehicle inertia measurement unit, Representing a second observation pose between the target acquisition time of the kth frame vehicle inertia measurement unit and the target acquisition time of the kth +1 frame vehicle inertia measurement unit,And representing a first observation pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
2. The method for determining calibration parameters of a lidar and an inertial measurement unit according to claim 1, wherein the determining, by using a multi-frame lidar point cloud acquired by the lidar, first observation data corresponding to each of a plurality of target acquisition moments, the first observation data including at least one of a first observation acceleration, a first observation rotational angular velocity, and a first observation pose, includes:
Acquiring multi-frame laser radar point clouds acquired by a laser radar, and performing odometer estimation on the multi-frame laser radar point clouds through a preset normal distribution transformation algorithm to obtain laser radar pose information respectively corresponding to a plurality of target acquisition moments;
And determining first observation data corresponding to each target acquisition time according to the laser radar pose information corresponding to each target acquisition time, wherein the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose.
3. The method for determining calibration parameters of a lidar and inertial measurement unit according to claim 2, wherein the determining the first observation data corresponding to each target acquisition time from the lidar pose information corresponding to each target acquisition time, the first observation data including at least one of a first observation acceleration, a first observation rotational angular velocity, and a first observation pose, includes:
Determining the pose information of the laser radar corresponding to each target acquisition moment as a first original pose corresponding to the target acquisition moment;
Performing curve fitting on the laser radar pose information corresponding to each target acquisition moment through a preset B spline algorithm to obtain a target track, and deriving the target track to obtain a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment;
Based on external parameters between the laser radar and a preset vehicle inertia measurement unit, respectively converting a first original pose, a first original acceleration and a first original rotation angular velocity corresponding to each target acquisition moment into a vehicle inertia measurement unit coordinate system to obtain a first observation pose, a first observation acceleration and a first observation rotation angular velocity corresponding to each target acquisition moment;
and determining the first observation pose, the first observation acceleration and the first observation rotation angular velocity corresponding to each target acquisition time as first observation data corresponding to the target acquisition time.
4. The method for determining calibration parameters of a lidar and an inertial measurement unit according to claim 1, wherein the acquiring, by the preset vehicle inertial measurement unit, the second observation data corresponding to each target acquisition time comprises:
Acquiring original measurement data in a vehicle stationary period through a preset vehicle inertia measurement unit, and carrying out average value calculation on the original measurement data to obtain gyroscope zero-bias data, wherein the gyroscope zero-bias data comprises acceleration zero-bias data and rotation angular velocity zero-bias data;
And determining second observation data of the vehicle inertia measurement unit at each target acquisition moment based on the gyroscope zero offset data.
5. The calibration parameter determining device of the laser radar and the inertia measuring unit is characterized by comprising:
The system comprises a determining module, a target acquisition module and a target acquisition module, wherein the determining module is used for determining first observation data respectively corresponding to a plurality of target acquisition moments through multi-frame laser radar point clouds acquired by a laser radar, the first observation data comprises at least one of first observation acceleration, first observation rotation angular velocity and first observation pose, and the target acquisition moments are used for indicating the acquisition moments of each frame of laser radar point clouds;
the acquisition module is used for acquiring second observation data corresponding to each target acquisition moment through a preset vehicle inertia measurement unit, wherein the second observation data comprises at least one of second observation acceleration, second observation rotation angular velocity and second observation pose, and the second observation data corresponds to the first observation data one by one;
The solving module is used for carrying out residual calculation on each first observation data and the corresponding second observation data based on each target acquisition time to obtain a target residual item corresponding to each target acquisition time, wherein the target residual item comprises at least one of a first residual between the first observation acceleration and the second observation acceleration, a second residual between the first observation rotation angular velocity and the second observation rotation angular velocity and a third residual between the first observation pose and the second observation pose; performing iterative optimization solution of calibration parameters to be optimized on a preset objective function based on a target residual error item corresponding to each target acquisition time to obtain optimal calibration parameters, wherein the optimal calibration parameters comprise external parameters, time deviation and gyroscope zero-bias data of the laser radar and the vehicle inertia measurement unit;
when the target residual term includes a first residual between the first observed acceleration and the second observed acceleration, a second residual between the first observed rotational angular velocity and the second observed rotational angular velocity, and a third residual between the first observation pose and the second observation pose, the preset target function includes:
wherein x represents an optimal calibration parameter, k represents target acquisition time of a kth frame of laser radar point cloud, A represents all frames of laser radar point cloud sets, A first residual representing a target acquisition instant of a kth frame lidar point cloud,A second residual representing the acquisition instant of the kth frame lidar point cloud,Third residual error representing acquisition time of kth frame laser radar point cloud, epsilon a representingIs represented by ε w Is represented by ε L Is a covariance of (2);
the first residual, the second residual and the third residual are respectively:
Wherein a 2 represents a second observed acceleration at the target acquisition time of the kth frame of vehicle inertia measurement unit, a 1 represents a first observed acceleration at the target acquisition time of the kth frame of laser radar point cloud, b a represents acceleration zero offset data, w 2 represents a second observed rotational angular velocity at the target acquisition time of the kth frame of vehicle inertia measurement unit, w 1 represents a first observed rotational angular velocity at the target acquisition time of the kth frame of laser radar point cloud, b g represents rotational angular velocity zero offset data, T LI represents external parameters of the laser radar and the vehicle inertia measurement unit, Representing a second observation pose between the target acquisition time of the kth frame vehicle inertia measurement unit and the target acquisition time of the kth +1 frame vehicle inertia measurement unit,And representing a first observation pose between the target acquisition time of the kth frame of laser radar point cloud and the target acquisition time of the kth+1 frame of laser radar point cloud.
6. A calibration parameter determining apparatus of a laser radar and an inertial measurement unit, characterized in that the calibration parameter determining apparatus of a laser radar and an inertial measurement unit comprises: a memory and at least one processor, the memory having a computer program stored therein;
The at least one processor invokes the computer program in the memory to cause the calibration parameter determination device of the lidar and inertial measurement unit to perform the calibration parameter determination method of the lidar and inertial measurement unit as claimed in any of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method for determining calibration parameters of a lidar and an inertial measurement unit according to any of claims 1 to 4.
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