CN113218408A - Multi-sensor fusion 2Dslam method and system suitable for multiple terrains - Google Patents
Multi-sensor fusion 2Dslam method and system suitable for multiple terrains Download PDFInfo
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Abstract
The invention discloses a multi-sensor fusion 2Dslam method suitable for multiple terrains, which comprises the steps of reading data of a laser radar and an encoder, resolving the data of the encoder and processing the data of the radar; scanning line feature segmentation point clouds to obtain ground point clouds and non-ground point clouds, and calculating gradient information; projecting the three-dimensional non-ground point cloud to two dimensions according to the gradient information; and (3) filtering and fusing the encoder and the two-dimensional point cloud particles of the laser radar. A2 Dslam system suitable for multi-sensor fusion of multiple terrains is also disclosed. The method is suitable for flat, bumpy and slope terrains, breaks through the traditional 2Dslam application occasions, further expands the application scene, and improves the positioning and mapping accuracy of the algorithm by fusing multiple sensors.
Description
Technical Field
The invention relates to the technical field of positioning navigation and map construction, in particular to a multi-sensor fusion 2Dslam method and system suitable for multiple terrains.
Background
SLAM (Simultaneous Localization And Mapping) is mainly used for solving the problem of performing positioning navigation And Mapping when a mobile device runs in an unknown environment. For positioning and mapping, data acquisition is needed firstly, and most of the prior art adopt an encoder and a laser radar sensor for data acquisition. However, the slam method, which conventionally uses an encoder to resolve a position and a single line lidar to detect obstacles, is only suitable for flat terrain. In addition, complex terrains such as bumpy terrains and slope terrains are mapped and positioned by a conventional method, and the accuracy is poor. Therefore, the simultaneous mapping and positioning of complex terrain using an encoder and a lidar is a very studied problem.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-sensor fusion 2Dslam method and system suitable for multi-terrain, which are suitable for flat, bumpy and slope terrains and break through the traditional 2Dslam application occasions.
In order to solve the technical problems, the invention adopts a technical scheme that: there is provided a 2Dslam method for multi-sensor fusion for multiple terrains, comprising the steps of:
s1: acquiring an encoder pulse value, and resolving a motion model parameter according to the pulse value; reading a laser radar point cloud, carrying out grid mean value down-sampling on the point cloud data, and removing point cloud distortion according to a motion model;
s2, dividing laser point cloud ground points and non-ground points based on the scanning line characteristics, calculating the gradient of the ground points, and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient;
s3: estimating to obtain the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model;
s4, scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the processed two-dimensional laser radar point cloud and the particle map at the previous moment, and updating the weight;
and S5, updating the map of each particle by referring to the current laser radar scanning data according to the obtained attitude of the particle, and taking the map of the particle with the highest weight as the current map.
In a preferred embodiment of the present invention, the step S1 includes the following steps:
s1.1: acquiring a pulse value of an encoder, and solving a self motion speed V and an angular speed omega;
s1.2: reading all three-dimensional point clouds in a laser radar detection range, storing the three-dimensional point clouds in a container A, adopting grids with length, width and height being d for the point clouds in the container A, taking the mean values of all point cloud coordinates (x, y and z) and time stamps in each grid, and storing all the grid mean values in a container B;
s1.3: and converting the coordinates into t starting time according to the uniform motion model of the speed V and the angular speed omega and the point cloud timestamp in the container B, and storing the converted point cloud in the container C.
Furthermore, the value range of d is 0.005-0.001 m.
In a preferred embodiment of the present invention, the step S2 includes the following steps:
s2.1: according to the container C, the points of two adjacent scanning lines with the same ray angle are respectively assumed to be (x)1,y1,z1) And (x)2,y2,z2) Angle of inclination
The inclination angle is less than 10 degrees and is considered as a ground point, so that the ground point and the non-ground point are divided;
s2.2: according to the adjacent line segment space between the ground point cloud scanning lines obtained by calculation
Gradient α, absolute height difference H ═ z1-z2I.e. projecting non-ground three-dimensional points (x, y, z) to two-dimensional coordinates
And storing the two-dimensional point cloud in a container D.
In a preferred embodiment of the present invention, the step S4 includes the following steps:
s4.1: according to the laser point cloud in the container D and the (t-1) time particle map
Scanning and matching by adopting a gradient descent method and optimizing the pose of the particles
Obtaining the optimal estimated pose of the particle
S4.2: according to the weight of particles at time (t-1)
Particle map
And the laser point cloud Z of the container D at the time ttPosition and posture of the particle
According to
Updating to obtain the weight of the particles at the time t
In a preferred embodiment of the present invention, the step S5 includes the following steps:
s5.1: calculating the degree of dispersion of particles
If N is presenteffIf the value is less than the threshold value T (the value is 2/3n), resampling the particles, otherwise, executing the step S5.2;
s5.2: according to the laser point cloud and the particle pose in the container D at the time t
And time t-1 particle map
Updating to obtain the particle map at the time t
S5.3: judging whether the data is completely read, if not, returning to the step S1; and if the data processing is finished, traversing all the particles, and outputting the particle map with the maximum weight as the current map.
In order to solve the technical problem, the invention adopts another technical scheme that: a multi-sensor fusion 2Dslam system suitable for multi-terrain is provided, which mainly comprises:
the encoder data module is used for acquiring an encoder pulse value and resolving a motion model parameter according to the pulse value;
the laser radar data module is used for reading laser radar point cloud, adopting grid mean value to carry out down-sampling processing on the point cloud data and removing point cloud distortion according to the motion model;
the laser point cloud segmentation module is used for segmenting laser point cloud ground points and non-ground points, calculating the gradient of the ground points and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient;
the particle pose acquisition module is used for estimating and obtaining the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model;
the particle pose optimization module is used for scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the two-dimensional laser radar point cloud output by the laser radar data module and the particle map at the previous moment, and updating the weight;
and the particle map output module is used for updating the map of each particle by referring to the current laser radar scanning data according to the attitude of the particle obtained by the particle pose optimization module, and taking the map of the particle with the highest weight as the current map. .
In order to solve the above technical problem, the present invention further provides a multi-sensor fusion 2Dslam device suitable for multiple terrains, comprising:
a memory, a processor, and a 2Dslam method program stored on the memory and executable on the processor suitable for multi-sensor fusion of multiple terrain;
the multi-terrain multi-sensor fusion-suitable 2Dslam method program, when executed by the processor, implements the steps of the multi-terrain multi-sensor fusion-suitable 2Dslam method of any one of the above.
In order to solve the technical problem, the invention further provides a computer medium, wherein a 2Dslam method program suitable for multi-sensor fusion of multiple terrains is stored on the computer medium;
the multi-terrain multi-sensor fusion-suitable 2Dslam method program, when executed by the processor, implements the steps of the multi-terrain multi-sensor fusion-suitable 2Dslam method of any one of the above.
The invention has the beneficial effects that: the method comprises the steps of resolving encoder data and processing radar data by reading laser radar and the encoder data; scanning line feature segmentation point clouds to obtain ground point clouds and non-ground point clouds, and calculating gradient information; projecting the three-dimensional non-ground point cloud to two dimensions according to the gradient information; and (3) filtering and fusing the encoder and the two-dimensional point cloud particles of the laser radar. The method is suitable for flat, bumpy and slope terrains, breaks through the traditional 2Dslam application occasions, further expands the application scene, and improves the positioning and mapping accuracy of the algorithm by fusing multiple sensors.
Drawings
FIG. 1 is a flow chart of a 2Dslam method of the present invention for multi-sensor fusion of multiple terrain;
FIG. 2 is a block diagram of the architecture of the 2Dslam system for multi-sensor fusion for multi-terrain.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Referring to fig. 1, an embodiment of the present invention includes:
a 2Dslam method for multi-sensor fusion for multiple terrain, comprising the steps of:
s1: acquiring an encoder pulse value, and resolving a motion model parameter according to the pulse value; reading a laser radar point cloud, carrying out grid mean value down-sampling on the point cloud data, and removing point cloud distortion according to a motion model; the method comprises the following specific steps:
s1.1: acquiring pulse values of a left encoder and a right encoder, and solving the self motion speed V and the angular speed omega;
s1.2: reading all three-dimensional point clouds in a laser radar detection range, storing the three-dimensional point clouds in a container A, adopting grids with length, width and height being d for the point clouds in the container A, taking the mean values of all point cloud coordinates (x, y and z) and time stamps in each grid, and storing all the grid mean values in a container B; preferably, d is 0.005-0.001 m;
s1.3: and converting the coordinates into t starting time according to the uniform motion model of the speed V and the angular speed omega and the point cloud timestamp in the container B, and storing the converted point cloud in the container C.
S2, dividing laser point cloud ground points and non-ground points based on the scanning line characteristics, calculating the gradient of the ground points, and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient; the method comprises the following specific steps:
s2.1: according to the container C, the points of two adjacent scanning lines with the same ray angle are respectively assumed to be (x)1,y1,z1) And (x)2,y2,z2) Angle of inclination
The inclination angle is less than 10 degrees and is considered as a ground point, so that the ground point and the non-ground point are divided;
s2.2: according to the adjacent line segment space between the ground point cloud scanning lines obtained by calculation
Gradient α, absolute height difference H ═ z1-z2I.e. projecting non-ground three-dimensional points (x, y, z) to two-dimensional coordinates
Two-dimensional point cloudStored in the container D.
S3: estimating to obtain the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model; i.e. the position and attitude of the particle according to the time t-1
And (5) calculating a constant motion model of the sum velocity V and the angular velocity omega, and estimating to obtain the position and posture of the particles at the time t
S4, scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the processed two-dimensional laser radar point cloud and the particle map at the previous moment, and updating the weight; the method comprises the following specific steps:
s4.1: according to the laser point cloud in the container D and the (t-1) time particle map
Scanning and matching by adopting a gradient descent method, and optimizing the pose of the particle according to the matching result of the gradient descent method
Obtaining the optimal estimated pose of the particle
S4.2: according to the weight of particles at time (t-1)
Particle map
And the laser point cloud Z of the container D at the time ttPosition and posture of the particle
According to
Updating to obtain the weight of the particles at the time t
And S5, updating the map of each particle by referring to the current laser radar scanning data according to the obtained attitude of the particle, and taking the map of the particle with the highest weight as the current map. The method comprises the following specific steps:
s5.1: calculating the degree of dispersion of particles
If N is presenteffIf the value is less than the threshold value T (generally 2/3n), resampling the particles, otherwise, executing the step S5.2;
s5.2: according to the laser point cloud and the particle pose in the container D at the time t
And time t-1 particle map
Updating to obtain the particle map at the time t
S5.3: judging whether the data is completely read, if not, returning to the step S1; and if the data processing is finished, traversing all the particles, and outputting the particle map with the maximum weight as the current map.
In the embodiment of the present invention, as shown in fig. 2, a 2Dslam system suitable for multi-sensor fusion of multiple terrains is further provided, which mainly includes:
the encoder data module is used for acquiring an encoder pulse value and resolving a motion model parameter according to the pulse value;
the laser radar data module is used for reading laser radar point cloud, adopting grid mean value to carry out down-sampling processing on the point cloud data and removing point cloud distortion according to the motion model;
the laser point cloud segmentation module is used for segmenting laser point cloud ground points and non-ground points, calculating the gradient of the ground points and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient;
the particle pose acquisition module is used for estimating and obtaining the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model;
the particle pose optimization module is used for scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the two-dimensional laser radar point cloud output by the laser radar data module and the particle map at the previous moment, and updating the weight;
and the particle map output module is used for updating the map of each particle by referring to the current laser radar scanning data according to the attitude of the particle obtained by the particle pose optimization module, and taking the map of the particle with the highest weight as the current map.
The 2Dslam system applicable to multi-terrain multi-sensor fusion can execute the 2Dsalm method applicable to multi-terrain multi-sensor fusion provided by the invention, can execute any combination of implementation steps of the method examples, and has corresponding functions and beneficial effects of the method.
The embodiment of the invention also provides a multi-sensor fusion 2Dslam device suitable for multiple terrains, which comprises a memory, a processor and a multi-sensor fusion 2Dslam method program which is stored on the memory and can be run on the processor and is suitable for multiple terrains;
the multi-terrain multi-sensor fusion-suitable 2Dslam method program, when executed by the processor, implements the steps of the multi-terrain multi-sensor fusion-suitable 2Dslam method of any one of the above.
The embodiment of the invention also provides a computer medium, wherein the computer medium is stored with a 2Dslam method program suitable for multi-sensor fusion of multiple terrains;
the multi-terrain multi-sensor fusion-suitable 2Dslam method program, when executed by the processor, implements the steps of the multi-terrain multi-sensor fusion-suitable 2Dslam method of any one of the above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A 2Dslam method for multi-sensor fusion for multiple terrain, comprising the steps of:
s1: acquiring an encoder pulse value, and resolving a motion model parameter according to the pulse value; reading a laser radar point cloud, carrying out grid mean value down-sampling on the point cloud data, and removing point cloud distortion according to a motion model;
s2, dividing laser point cloud ground points and non-ground points based on the scanning line characteristics, calculating the gradient of the ground points, and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient;
s3: estimating to obtain the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model;
s4, scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the processed two-dimensional laser radar point cloud and the particle map at the previous moment, and updating the weight;
and S5, updating the map of each particle by referring to the current laser radar scanning data according to the obtained attitude of the particle, and taking the map of the particle with the highest weight as the current map.
2. The multi-sensor fusion-enabled 2Dslam method for multi-terrain, according to claim 1, wherein the specific steps of step S1 include:
s1.1: acquiring a pulse value of an encoder, and solving a self motion speed V and an angular speed omega;
s1.2: reading all three-dimensional point clouds in a laser radar detection range, storing the three-dimensional point clouds in a container A, adopting grids with length, width and height being d for the point clouds in the container A, taking the mean values of all point cloud coordinates (x, y and z) and time stamps in each grid, and storing all the grid mean values in a container B;
s1.3: and converting the coordinates into t starting time according to the uniform motion model of the speed V and the angular speed omega and the point cloud timestamp in the container B, and storing the converted point cloud in the container C.
3. The multi-sensor fusion 2Dslam method for multi-terrain applications as defined in claim 2 wherein d ranges from 0.005 m to 0.001 m.
4. The multi-sensor fusion-enabled 2Dslam method according to claim 2, wherein the specific steps of step S2 include:
s2.1: according to the container C, the points of two adjacent scanning lines with the same ray angle are respectively assumed to be (x)1,y1,z1) And (x)2,y2,z2) Angle of inclination
The inclination angle is less than 10 degrees and is considered as a ground point, so that the ground point and the non-ground point are divided;
s2.2: according to the adjacent line segment space between the ground point cloud scanning lines obtained by calculation
Gradient α, absolute height difference H ═ z1-z2I.e. projecting non-ground three-dimensional points (x, y, z) to two-dimensional coordinates
And storing the two-dimensional point cloud in a container D.
5. The multi-sensor fusion-enabled 2Dslam method according to claim 4, wherein the specific steps of step S4 include:
s4.1: from the laser point clouds in the container D andt-1) time particle map
Scanning and matching by adopting a gradient descent method and optimizing the pose of the particles
Obtaining the optimal estimated pose of the particle
S4.2: according to the weight of particles at time (t-1)
Particle map
And the laser point cloud Z of the container D at the time ttPosition and posture of the particle
According to
Updating to obtain the weight of the particles at the time t
6. The multi-sensor fusion-enabled 2Dslam method according to claim 4, wherein the specific steps of step S5 include:
s5.1: calculating the degree of dispersion of particles
If N is presenteffIf the value is less than the threshold value T (the value is 2/3n), resampling the particles, otherwise, executing the step S5.2;
s5.2: according to time t container DLaser point cloud and particle pose in
And time t-1 particle map
Updating to obtain the particle map at the time t
S5.3: judging whether the data is completely read, if not, returning to the step S1; and if the data processing is finished, traversing all the particles, and outputting the particle map with the maximum weight as the current map.
7. A multi-sensor fused 2Dslam system suitable for use in multiple terrains, comprising:
the encoder data module is used for acquiring an encoder pulse value and resolving a motion model parameter according to the pulse value;
the laser radar data module is used for reading laser radar point cloud, adopting grid mean value to carry out down-sampling processing on the point cloud data and removing point cloud distortion according to the motion model;
the laser point cloud segmentation module is used for segmenting laser point cloud ground points and non-ground points, calculating the gradient of the ground points and projecting the non-three-dimensional ground points into two-dimensional ground points according to the gradient;
the particle pose acquisition module is used for estimating and obtaining the particle pose of the current moment according to the particle pose of the previous moment and the encoder data motion model;
the particle pose optimization module is used for scanning, matching and optimizing the particle pose by adopting a gradient descent method according to the two-dimensional laser radar point cloud output by the laser radar data module and the particle map at the previous moment, and updating the weight;
and the particle map output module is used for updating the map of each particle by referring to the current laser radar scanning data according to the attitude of the particle obtained by the particle pose optimization module, and taking the map of the particle with the highest weight as the current map.
8. A multi-sensor fused 2Dslam device adapted for use with multiple terrains, comprising:
a memory, a processor, and a 2Dslam method program stored on the memory and executable on the processor suitable for multi-sensor fusion of multiple terrain;
the 2Dslam method program for multi-terrain multi-sensor fusion, when executed by the processor, implementing the steps of the 2Dslam method for multi-terrain multi-sensor fusion of any of claims 1-6.
9. A computer medium having stored thereon a 2Dslam method program suitable for multi-sensor fusion of multiple terrain;
the 2Dslam method program for multi-terrain multi-sensor fusion, when executed by the processor, implementing the steps of the 2Dslam method for multi-terrain multi-sensor fusion of any of claims 1-6.
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| CN113607166B (en) * | 2021-10-08 | 2022-01-07 | 广东省科学院智能制造研究所 | Indoor and outdoor positioning method and device for autonomous mobile robot based on multi-sensor fusion |
| US12019453B2 (en) | 2021-10-08 | 2024-06-25 | Institute Of Intelligent Manufacturing, Gdas | Multi-sensor-fusion-based autonomous mobile robot indoor and outdoor positioning method and robot |
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| CN114329978A (en) * | 2021-12-29 | 2022-04-12 | 杭州鲁尔物联科技有限公司 | Fusion method and device of slope units, electronic equipment and storage medium |
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