CN114812575A - Correction parameter determining method and device, electronic equipment and storage medium - Google Patents
Correction parameter determining method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114812575A CN114812575A CN202210255427.0A CN202210255427A CN114812575A CN 114812575 A CN114812575 A CN 114812575A CN 202210255427 A CN202210255427 A CN 202210255427A CN 114812575 A CN114812575 A CN 114812575A
- Authority
- CN
- China
- Prior art keywords
- lane line
- line data
- matched
- determining
- data set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000009466 transformation Effects 0.000 claims description 20
- 230000001131 transforming effect Effects 0.000 claims description 6
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 230000004927 fusion Effects 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 4
- 108700041286 delta Proteins 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
Abstract
The method comprises the steps of obtaining a first lane line data set and a second lane line data set; the first lane line data set includes data corresponding to a plurality of lane lines detected by the vehicle-mounted camera device, and the second lane line data set includes data corresponding to a plurality of lane lines detected by the high-precision map. Determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data. Determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter. According to the method and the device, the absolute position deviation is corrected by utilizing the lane line data corresponding to the multiple matched lane lines instead of the relative position deviation in the existing scheme, and the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved.
Description
Technical Field
The invention relates to the field of automatic driving high-precision positioning, in particular to a method and a device for determining correction parameters, electronic equipment and a storage medium.
Background
The high-precision positioning is used as an important component of automatic driving, is one of indispensable core technologies for automatic driving safety driving, and plays an important role in the aspects of transverse accurate positioning, longitudinal accurate positioning, obstacle detection and collision avoidance, intelligent vehicle speed control, path planning, behavior decision and the like of a vehicle. In order to realize automatic driving high-precision positioning, an automatic driving vehicle is provided with various sensors, such as a global positioning navigation system, a laser radar, a high-precision map and a vehicle-mounted camera, and a multi-sensor fusion positioning method is derived in the industry at present based on the various sensors provided by the automatic driving vehicle.
In the related art, the transverse positioning correction is mainly performed by transversely adjusting the distance between the vehicle and the left and right lane lines observed by using a camera and the distance between the vehicle and the left and right lane lines observed by using a high-precision map, namely, the transverse adjustment is performed along the Y-axis direction in a vehicle body coordinate system, and the vehicle body coordinate axis is the X-axis in the vehicle driving direction, is horizontally and leftwards the Y-axis and is vertically and upwards the Z-axis, so that the distance between the vehicle and the left and right lane lines conforms to the cost function formed by the observation of the camera and the high-precision map. However, the lateral alignment correction method is only a relative correction along the Y-axis of the vehicle body coordinate system, and cannot adjust the yaw angle.
Disclosure of Invention
The embodiment of the application provides a correction parameter determining method, a correction parameter determining device, electronic equipment and a storage medium, absolute position deviation can be corrected instead of relative position deviation in the existing scheme, and fusion accuracy of vehicle-mounted camera equipment and a high-precision map can be improved.
The embodiment of the application provides a method for determining a correction parameter, which comprises the following steps:
acquiring a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by the vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by the high-precision map;
determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data;
determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
Further, the first lane line data in the first lane line data set includes first cross-distance data, and the second lane line data in the second lane line data set includes second cross-distance data;
determining a set of matched lane line data sets from the first lane line data set and the second lane line data set, comprising:
determining lane line data to be matched from the first lane line data set;
and determining second lane line data matched with the lane line data to be matched according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a matched lane line data group.
Further, according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data, determining the second lane line data matched with the lane line data to be matched to obtain a matched lane line data group, including:
determining the difference value of first cross-section distance data in the lane line data to be matched and second cross-section distance data in each second lane line data to obtain a cross-section distance difference value set;
determining a difference value square set according to the cross-section distance difference value set;
and determining the second lane line data corresponding to the minimum value in the difference square set as the second lane line data matched with the lane line data to be matched to obtain a matched lane line data set.
Further, determining a correction parameter according to the data set of the matched lane line, including:
determining transformation data corresponding to each lane line to be matched in the matched lane line data set; the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated;
determining a matching difference data set according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched;
and determining correction parameters according to the matching difference data set.
Further, determining a correction parameter based on the matching difference dataset, comprising:
and performing iterative update processing on the correction parameter to be updated according to the sum of the matching difference data in the matching difference data set to obtain the correction parameter.
Further, the first lane line data set includes data corresponding to a plurality of lane lines located on one side of the vehicle and detected by the vehicle-mounted camera device;
or;
the first lane line data set includes data corresponding to a plurality of lane lines located on both sides of the vehicle detected by the vehicle-mounted image pickup device.
Accordingly, an embodiment of the present application provides a correction parameter determining apparatus, including:
the acquisition module is used for acquiring a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by the vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by the high-precision map;
the first determining module is used for determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data;
the second determining module is used for determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
Further, the first lane line data in the first lane line data set includes first cross-distance data, and the second lane line data in the second lane line data set includes second cross-distance data;
a first determination module comprising:
the first determining submodule is used for determining lane line data to be matched from the first lane line data set;
and the second determining submodule is used for determining second lane line data matched with the lane line data to be matched according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a matched lane line data group.
Further, a second determination submodule, comprising:
the first determining unit is used for determining the difference value between first cross-section distance data in the lane line data to be matched and second cross-section distance data in each second lane line data to obtain a cross-section distance difference value set;
the second determining unit is used for determining a difference value square set according to the cross-section distance difference value set;
and the third determining unit is used for determining the second lane line data corresponding to the minimum value in the difference square set as the second lane line data matched with the lane line data to be matched to obtain a matched lane line data set.
Further, the second determining module includes:
the third determining submodule is used for determining the transformation data corresponding to each lane line to be matched in the matched lane line data set; the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated;
the fourth determining submodule is used for determining a matching difference data set according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched;
and the fifth determining submodule is used for determining the correction parameters according to the matching difference data set.
And further, the fifth determining submodule is used for performing iterative update processing on the correction parameter to be updated according to the sum of the matching difference data in the matching difference data set to obtain the correction parameter.
Accordingly, an embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for determining the correction parameter.
Accordingly, an embodiment of the present application further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for determining the correction parameter.
The embodiment of the application has the following beneficial effects:
the method comprises the steps of obtaining a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by the vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by the high-precision map. Determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data. Determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter. According to the embodiment of the application, the absolute position deviation is corrected by using the lane line data corresponding to the multiple matched lane lines instead of the relative position deviation in the existing scheme, so that the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments or the prior art of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of an application environment provided by an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for determining a correction parameter according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a correction parameter determination apparatus according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It should be apparent that the described embodiment is only one embodiment of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
An "embodiment" as referred to herein relates to a particular feature, structure, or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it is to be understood that the terms "first", "second", and "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first," "second," and "third," etc., may explicitly or implicitly include one or more of the features. Moreover, the terms "first," "second," and "third," etc. are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprising," "having," and "being," as well as any variations thereof, are intended to cover non-exclusive inclusions.
Referring to fig. 1, a schematic diagram of an application environment provided in an embodiment of the present application is shown, including a server 101, an in-vehicle image capturing apparatus 103, and a high-precision map 105. In this embodiment, the vehicle may detect lane line data corresponding to a plurality of lane lines through a vehicle-mounted camera device disposed on the vehicle to obtain a first lane line data set, and detect lane line data corresponding to a plurality of lane lines through a high-precision map disposed on the vehicle to obtain a second lane line data set. And then, the first lane line data set and the second lane line data set are transmitted to a server platform or a server cluster, and a matched lane line data set is determined from the first lane line data set and the second lane line data set by using the first lane line data set and the second lane line data set to determine correction parameters, so that absolute position deviation is corrected instead of relative position deviation in the existing scheme, and the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved. In this manner, only the server of the basic configuration can be installed on the vehicle, and the complex calculation can be carried out by a common server platform or a server cluster.
In another alternative embodiment, the server may be an on-board server provided in the vehicle, and each on-board server may individually provide the correction parameter determination service for the vehicle in which the server is located. Specifically, the vehicle-mounted server can detect lane line data corresponding to a plurality of lane lines through a vehicle-mounted camera device arranged on a vehicle to obtain a first lane line data set, and detect lane line data corresponding to the plurality of lane lines through a high-precision map arranged on the vehicle to obtain a second lane line data set. And then from the first lane line data set and the second lane line data set, determine a matched lane line data set to determine correction parameters, correct absolute position deviation, but not relative position deviation in the existing scheme, and can improve the fusion accuracy of the vehicle-mounted camera equipment and the high-precision map. In this embodiment, since the complicated calculation is carried out by the on-board server on the vehicle, the vehicle needs to be provided with hardware and software devices that meet the conditions. Although the vehicle price is increased compared to the first case, the interaction time is reduced because interaction with a common server platform is not required, so that the vehicle can obtain the result more quickly, and the server and the vehicle are in one-to-one correspondence, thereby avoiding the possibility of transmission errors and only improving the service quality.
A specific embodiment of a method for determining a correction parameter of the present application is described below, and fig. 2 is a schematic flow chart of a method for determining a correction parameter provided in the embodiment of the present application, and the present specification provides the method operation steps as shown in the embodiment or the flow chart, but may include more or less operation steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is only one of many possible orders of execution and does not represent the only order of execution, and in actual execution, the steps may be performed sequentially or in parallel as in the embodiments or methods shown in the figures (e.g., in the context of parallel processors or multi-threaded processing). Specifically, as shown in fig. 2, the method includes:
s201: acquiring a first lane line data set and a second lane line data set; the first lane line data set includes data corresponding to a plurality of lane lines detected by the vehicle-mounted camera device, and the second lane line data set includes data corresponding to a plurality of lane lines detected by the high-precision map.
In this embodiment of the application, the server may obtain data corresponding to a plurality of lane lines in a vehicle coordinate system detected by the vehicle camera device to obtain a first lane line data set, and obtain data corresponding to a plurality of lane lines in the vehicle coordinate system detected by the high-precision map and data corresponding to a plurality of lane lines in a world coordinate system detected by the high-precision map to obtain a second lane line data set.
In an alternative embodiment, the server may obtain data corresponding to a plurality of lane lines located on one side of the vehicle and detected by the vehicle-mounted camera device, so as to obtain the first lane line data set. And meanwhile, data corresponding to a plurality of lane lines on one side of the vehicle detected by the high-precision map can be acquired, and a second lane line data set is obtained. The data corresponding to the multiple lane lines detected by the vehicle-mounted camera device and the data corresponding to the multiple lane lines detected by the high-precision map are data corresponding to the multiple lane lines on the same side of the vehicle. By detecting data corresponding to a plurality of lane lines positioned on the same side of the vehicle, the data volume of the lane line data can be reduced, the calculation amount of the server is reduced, and the calculation resources of the server are solved.
In another optional implementation manner, the server may obtain data corresponding to a plurality of lane lines located on both sides of the vehicle, which are detected by the vehicle-mounted camera device, to obtain a first lane line data set. And meanwhile, data corresponding to a plurality of lane lines on two sides of the vehicle detected by the high-precision map can be acquired, and a second lane line data set is obtained. For example, lane line data corresponding to four lane lines of the vehicle body coordinate system on two sides of the vehicle detected by the vehicle-mounted camera, such as left lane line data y corresponding to a left lane line, may be obtained 11 Left lane line data y corresponding to the left lane line 12 Right lane line data y corresponding to the right lane line 13 Right and right lane line data y corresponding to the right and right lane lines 14 . Lane line data corresponding to four lane lines on two sides of the vehicle under a vehicle body coordinate system and detected by a high-precision map, such as left lane line data y corresponding to a left lane line 21 Left lane line data y corresponding to the left lane line 22 Right lane line data y corresponding to the right lane line 23 Right and right lane line data y corresponding to the right and right lane lines 24 And acquiring lane line data corresponding to four lane lines on two sides of the vehicle under a world coordinate system and detected by a high-precision map, such as left lane line data y corresponding to a left lane line 21 ', left lane line data y corresponding to the left lane line 22 ', right lane line data y corresponding to the right lane line 23 ' Right and Right Lane line data y corresponding to the Right and Right Lane lines 24 '. By detecting the data corresponding to the multiple lane lines on the two sides of the vehicle, the data corresponding to the lane lines can be enriched, the influence of equipment faults or external factors on the lane line data is reduced, and the accuracy of the follow-up determination of the correction parameters is improved. The data corresponding to the lane line can be expressed by the following formula:
y＝C0+C1*x+C1*x 2 +C2*x 3
where C0 may represent a cross-sectional distance in a vehicle body coordinate system, i.e., a lateral distance of a vehicle from a lane line, C1 may represent a slope of the lane line, C2 may represent a 2-fold curvature of the lane line, and C3 may represent a 6-fold curvature change rate of the lane line. The vehicle body coordinate system can be a coordinate system with an X axis in the vehicle running direction, a Y axis horizontally towards the left and a Z axis vertically upwards.
In another alternative embodiment, the server may obtain data corresponding to a plurality of lane lines located on one side of the vehicle and detected by the vehicle-mounted camera device, so as to obtain the first lane line data set. And meanwhile, data corresponding to a plurality of lane lines on two sides of the vehicle detected by the high-precision map can be acquired, and a second lane line data set is obtained.
In another optional implementation manner, the server may obtain data corresponding to a plurality of lane lines located on both sides of the vehicle, which are detected by the vehicle-mounted camera device, to obtain a first lane line data set. And meanwhile, data corresponding to a plurality of lane lines on one side of the vehicle detected by the high-precision map can be acquired, and a second lane line data set is obtained.
For ease of understanding, the following description will be given taking, as an example, data corresponding to a plurality of lane lines on both sides of a vehicle detected by an in-vehicle image pickup apparatus and data corresponding to a plurality of lane lines on both sides of a vehicle detected by a high-precision map.
S203: determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data.
In an embodiment of the application, the first lane line data in the first lane line data set may include first cross-distance data, and the second lane line data in the second lane line data set may include second cross-distance data. After the first lane line data set and the second lane line data set are obtained, the server can determine lane line data to be matched from the first lane line data set, and can determine second lane line data matched with the lane line data to be matched according to first cross-section distance data in the lane line data to be matched and second cross-section distance data in each second lane line data to obtain a matched lane line data set. That is, the server may perform correlation matching between lane line data corresponding to a plurality of lane lines in the vehicle body coordinate system detected by the vehicle-mounted image pickup device and lane line data corresponding to a plurality of lane lines in the vehicle body coordinate system detected by the high-precision map. In an alternative embodiment, 16 sets may be established according to four lane lines detected by the vehicle-mounted camera and four lanes detected by the high-precision map, and each set may include one lane line detected by the vehicle-mounted camera and one lane line detected by the high-precision map. Specifically, the lane line data corresponding to each of the four lane lines detected by the vehicle-mounted camera device may be associated and matched with the lane line data corresponding to each of the four lane lines detected by the high-precision map, so as to obtain a matched lane line data set.
In an alternative embodiment, the server may determine lane line data to be matched from the first lane line data set, and determine a difference between first cross-sectional distance data in the lane line data to be matched and second cross-sectional distance data in each second lane line data to obtain a cross-sectional distance difference set. And then determining a difference square set according to the cross-section distance difference set, and then determining second lane line data corresponding to the minimum value in the difference square set as second lane line data matched with the lane line data to be matched to obtain a matched lane line data group. In a specific implementation, the server may calculate a sum of squares of differences between the first cross-sectional distance data and the second cross-sectional distance data in each combination, and the first lane line data and the second lane line data in the set with the smallest sum of squares are the best matches.
For example, the server may select lane line data y corresponding to the left lane line from lane line data corresponding to four lane lines in the vehicle body coordinates located on both sides of the vehicle detected by the vehicle-mounted camera 11 As the lane line data to be matched and respectively determining y 11 First cross-sectional distance y in 11 CO1 With high-precision map detectionLeft and left lane lines y in lane line data corresponding to four lane lines on two sides of the vehicle under the coordinate of the vehicle body 21 Second cross-sectional distance y in 21 CO2 Left y 22 Second cross-sectional distance y in the lane line 22 CO2 Right y 23 Second cross-sectional distance y in the lane line 23 CO2 And right lane line y 24 Second cross-sectional distance y in 24 CO2 Cross-sectional distance difference set { Δ 1 ═ y } 11 CO1 -y 21 CO2 、Δ2＝y 11 CO1 -y 22 CO2 、Δ3＝y 11 CO1 -y 23 CO2 、Δ4＝y 11 CO1 -y 24 CO2 }. Then, according to the cross-section distance difference value set, a difference value square set delta 1 is determined 2 、Δ2 2 、Δ3 2 、Δ4 2 And the minimum value Δ 1 in the squared difference set 2 Corresponding second lane line data y 21 And determining the second lane line data matched with the lane line data to be matched to obtain a matched lane line data group.
S205: determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
In the embodiment of the application, the server can determine the transformation data corresponding to each lane line to be matched in the matched lane line data set, wherein the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated. After the transformation data corresponding to each lane line data to be matched is determined, a matching difference data set may be determined according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched, and a correction parameter may be determined according to the matching difference data set.
Suppose that the transverse intersection point of one lane line detected by the vehicle-mounted camera device and the vehicle under the coordinates of the vehicle body is p c (0,C0 camera ) If the intersection point is transformed based on the correction parameters (x, y, theta) to be updated between the coordinate system of the vehicle body and the coordinate system of the world, the coordinate p of the intersection point under the world coordinate can be obtained w (x w ,y w ). Specifically, the transformation data may be determined using the following formula:
x w ＝x+C0 camera *sinθ
y w ＝y-C0 camera *cosθ
wherein, (x, y, θ) may represent a correction parameter to be updated, x may represent a longitudinal correction parameter, y may represent a lateral correction parameter, and θ may represent a yaw angle parameter.
Theoretically, p w This point must satisfy the expression equation of the same lane line in the world coordinate system, i.e.
y w '＝C0 w +C1 w *x w +C2 w *x w 2 +C3 w *x w 3
If (x, y, theta) has no error, y w Should be equal to y w '。
Wherein, C0 w Can represent the cross-sectional distance in the world coordinate system, i.e. the lateral distance of the vehicle from the lane line, C1 w May represent the slope of the lane line, C2 w Can represent 2 times the curvature of the lane line, C3 w A lane line 6 times rate of change in curvature may be represented.
In practical applications, y is an unavoidable error w And y w ' may not be exactly equal. In an optional implementation manner, iterative update processing may be performed on the parameter to be updated according to the sum of the matching difference data in the matching difference data set, so as to obtain the correction parameter. The residual equation is then established and an optimal correction parameter (x, y, θ) is found to minimize the residual.
The residual equation can be expressed by the following formula:
F＝y w -y w '＝(y-C0 camera *cosθ)-(C0 w +C1 w *x w +C2 w *x w 2 +C3 w *x w 3 )
continuing with the description based on the above-listed example, since there are four sets of matching lane line data, 4 residual equations can be established, and the solved number is 3, and the optimal correction parameters can be obtained by using the least square solution library. The optimal correction parameter can correct x, y and theta.
By adopting the method for determining the correction parameters provided by the embodiment of the application, the absolute position deviation is corrected by using the lane line data corresponding to the plurality of matched lane lines instead of the relative position deviation in the existing scheme, so that the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved.
Fig. 3 is a schematic structural diagram of a correction parameter determining apparatus provided in an embodiment of the present application, and as shown in fig. 3, the apparatus may include:
an obtaining module 301, configured to obtain a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by the vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by the high-precision map;
a first determining module 303, configured to determine a set of matched lane line data sets from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data;
a second determining module 305, configured to determine a correction parameter according to the set of matched lane line data sets; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
In the embodiment of the application, the first lane line data in the first lane line data set comprises first cross-distance data, and the second lane line data in the second lane line data set comprises second cross-distance data;
a first determining module 303 comprising:
the first determining submodule is used for determining lane line data to be matched from the first lane line data set;
and the second determining submodule is used for determining second lane line data matched with the lane line data to be matched according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a matched lane line data group.
In this embodiment of the application, the second determining sub-module includes:
the first determining unit is used for determining the difference value between first cross-section distance data in the lane line data to be matched and second cross-section distance data in each second lane line data to obtain a cross-section distance difference value set;
the second determining unit is used for determining a difference value square set according to the cross-section distance difference value set;
and the third determining unit is used for determining the second lane line data corresponding to the minimum value in the difference square set as the second lane line data matched with the lane line data to be matched to obtain a matched lane line data set.
In this embodiment of the application, the second determining module 305 includes:
the third determining submodule is used for determining the transformation data corresponding to each lane line to be matched in the matched lane line data set; the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated;
the fourth determining submodule is used for determining a matching difference data set according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched;
and the fifth determining submodule is used for determining the correction parameters according to the matching difference data set.
The fifth determining submodule in the embodiment of the application is configured to perform iterative update processing on the correction parameter to be updated according to the sum of the matching difference data in the matching difference data set, so as to obtain the correction parameter.
The device and method embodiments in the embodiments of the present application are based on the same application concept.
By adopting the correction parameter determining device provided by the embodiment of the application, the absolute position deviation is corrected by utilizing the lane line data corresponding to a plurality of matched lane lines instead of the relative position deviation in the existing scheme, so that the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved.
The present invention further provides an electronic device, which can be disposed in a server to store at least one instruction, at least one program, a code set, or a set of instructions related to a method for determining a modified parameter in the method embodiment, where the at least one instruction, the at least one program, the code set, or the set of instructions are loaded from the memory and executed to implement the above method for determining a modified parameter.
The present invention also provides a storage medium, which can be disposed in a server to store at least one instruction, at least one program, a code set, or a set of instructions related to implementing a method for determining a modified parameter in the method embodiment, where the at least one instruction, the at least one program, the code set, or the set of instructions are loaded and executed by the processor to implement the method for determining a modified parameter.
Optionally, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, a storage medium including: various media that can store program codes, such as a usb disk, a Read-only Memory (ROM), a removable hard disk, a magnetic disk, or an optical disk.
As can be seen from the embodiments of the method, the apparatus, the electronic device, or the storage medium for determining the correction parameter provided in the present application, the method in the present application includes acquiring a first lane line data set and a second lane line data set; the first lane line data set includes data corresponding to a plurality of lane lines detected by the vehicle-mounted camera device, and the second lane line data set includes data corresponding to a plurality of lane lines detected by the high-precision map. Determining a matched lane line data set from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data. Determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter. According to the embodiment of the application, the absolute position deviation is corrected by using the lane line data corresponding to the multiple matched lane lines instead of the relative position deviation in the existing scheme, so that the fusion precision of the vehicle-mounted camera equipment and the high-precision map can be improved.
In the present invention, unless otherwise expressly stated or limited, the terms "connected" and "connected" are to be construed broadly, e.g., as meaning either a fixed connection or a removable connection, or an integral part; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It should be noted that: the foregoing sequence of the embodiments of the present application is for description only and does not represent the superiority and inferiority of the embodiments, and the specific embodiments are described in the specification, and other embodiments are also within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in the order of execution in different embodiments and achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown or connected to enable the desired results to be achieved, and in some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment is described with emphasis on differences from other embodiments. Especially, for the embodiment of the device, since it is based on the embodiment similar to the method, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (11)
1. A method for determining a correction parameter, comprising:
acquiring a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by a high-precision map;
determining a set of matched lane line data sets from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data;
determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
2. The method of claim 1, wherein first lane line data in the first lane line data set includes first cross-distance data and second lane line data in the second lane line data set includes second cross-distance data;
said determining a set of matched lane line data sets from said first lane line data set and said second lane line data set, comprising:
determining lane line data to be matched from the first lane line data set;
and determining second lane line data matched with the lane line data to be matched according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a matched lane line data group.
3. The method according to claim 2, wherein the determining second lane line data matched with the lane line data to be matched according to the first cross-sectional distance data in the lane line data to be matched and the second cross-sectional distance data in each second lane line data to obtain a matched lane line data group comprises:
determining a difference value between the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a cross-section distance difference value set;
determining a difference square set according to the cross-section distance difference set;
and determining the second lane line data corresponding to the minimum value in the difference square set as the second lane line data matched with the lane line data to be matched to obtain the matched lane line data group.
4. The method of claim 2, wherein determining a correction parameter from the set of matched lane line data sets comprises:
determining transformation data corresponding to each lane line to be matched in the matched lane line data set; the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated;
determining a matching difference data set according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched;
and determining the correction parameters according to the matching difference data set.
5. The method of claim 4, wherein said determining said correction parameter from said match difference data set comprises:
and performing iterative update processing on the correction parameter to be updated according to the sum of the matching difference data in the matching difference data set to obtain the correction parameter.
6. The method according to claim 1, wherein the first lane line data set includes data corresponding to a plurality of lane lines on one side of the vehicle detected by the on-vehicle camera device;
or;
the first lane line data set includes data corresponding to a plurality of lane lines located on both sides of the vehicle detected by the vehicle-mounted camera device.
7. An apparatus for determining a correction parameter, comprising:
the acquisition module is used for acquiring a first lane line data set and a second lane line data set; the first lane line data set comprises data corresponding to a plurality of lane lines detected by vehicle-mounted camera equipment, and the second lane line data set comprises data corresponding to a plurality of lane lines detected by a high-precision map;
a first determining module for determining a set of matched lane line data sets from the first lane line data set and the second lane line data set; the matched lane line data set in the matched lane line data set comprises matched first lane line data and second lane line data;
the second determining module is used for determining correction parameters according to the matched lane line data set; the correction parameters comprise a transverse correction parameter, a longitudinal correction parameter and a yaw angle parameter.
8. The apparatus of claim 7, wherein first lane line data in the first lane line data set includes first cross-distance data and second lane line data in the second lane line data set includes second cross-distance data;
the first determining module includes:
the first determining submodule is used for determining lane line data to be matched from the first lane line data set;
and the second determining submodule is used for determining second lane line data matched with the lane line data to be matched according to the first cross-section distance data in the lane line data to be matched and the second cross-section distance data in each second lane line data to obtain a matched lane line data group.
9. The apparatus of claim 8, wherein the second determining module comprises:
the third determining submodule is used for determining the transformation data corresponding to each lane line to be matched in the matched lane line data group set; the transformation data is obtained by transforming the lane line data to be matched based on the correction parameters to be updated;
the fourth determining submodule is used for determining a matching difference data set according to the transformation data corresponding to each lane line data to be matched and the second lane line data matched with each lane line data to be matched;
and the fifth determining submodule is used for determining the correction parameters according to the matching difference data set.
10. An electronic device, comprising a processor and a memory, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the memory, and wherein the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the method for determining a modification parameter according to any one of claims 1 to 6.
11. A computer readable storage medium, having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the method of determining modification parameters according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210255427.0A CN114812575A (en) | 2022-03-15 | 2022-03-15 | Correction parameter determining method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210255427.0A CN114812575A (en) | 2022-03-15 | 2022-03-15 | Correction parameter determining method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114812575A true CN114812575A (en) | 2022-07-29 |
Family
ID=82529730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210255427.0A Pending CN114812575A (en) | 2022-03-15 | 2022-03-15 | Correction parameter determining method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114812575A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116823958A (en) * | 2023-04-20 | 2023-09-29 | 阿波罗智联(北京)科技有限公司 | Yaw angle estimation method, device, equipment, vehicle and medium of vehicle-mounted camera |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103954275A (en) * | 2014-04-01 | 2014-07-30 | 西安交通大学 | Lane line detection and GIS map information development-based vision navigation method |
| CN110516652A (en) * | 2019-08-30 | 2019-11-29 | 北京百度网讯科技有限公司 | Method, apparatus, electronic equipment and the storage medium of lane detection |
| CN110645996A (en) * | 2019-09-17 | 2020-01-03 | 武汉中海庭数据技术有限公司 | Method and system for extracting perception data |
| CN111742326A (en) * | 2019-05-22 | 2020-10-02 | 深圳市大疆创新科技有限公司 | Lane line detection method, electronic device and storage medium |
| US20210312194A1 (en) * | 2020-12-15 | 2021-10-07 | Beijing Baidu Netcom Science Technology Co., Ltd. | Method and apparatus of matching lane line data, device and storage medium |
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
-
2022
- 2022-03-15 CN CN202210255427.0A patent/CN114812575A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103954275A (en) * | 2014-04-01 | 2014-07-30 | 西安交通大学 | Lane line detection and GIS map information development-based vision navigation method |
| CN111742326A (en) * | 2019-05-22 | 2020-10-02 | 深圳市大疆创新科技有限公司 | Lane line detection method, electronic device and storage medium |
| CN110516652A (en) * | 2019-08-30 | 2019-11-29 | 北京百度网讯科技有限公司 | Method, apparatus, electronic equipment and the storage medium of lane detection |
| CN110645996A (en) * | 2019-09-17 | 2020-01-03 | 武汉中海庭数据技术有限公司 | Method and system for extracting perception data |
| US20210312194A1 (en) * | 2020-12-15 | 2021-10-07 | Beijing Baidu Netcom Science Technology Co., Ltd. | Method and apparatus of matching lane line data, device and storage medium |
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116823958A (en) * | 2023-04-20 | 2023-09-29 | 阿波罗智联(北京)科技有限公司 | Yaw angle estimation method, device, equipment, vehicle and medium of vehicle-mounted camera |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107481292B (en) | Attitude error estimation method and device for vehicle-mounted camera | |
| CN111415387B (en) | Camera pose determining method and device, electronic equipment and storage medium | |
| CN111380539B (en) | Vehicle positioning and navigation method and device and related system | |
| US9335171B2 (en) | Travel trace storage apparatus | |
| CN110954112B (en) | Method and device for updating matching relation between navigation map and perception image | |
| US11447141B2 (en) | Method and device for eliminating steady-state lateral deviation and storage medium | |
| CN111380538B (en) | Vehicle positioning method, navigation method and related device | |
| CN112068171B (en) | Vehicle positioning method and device, vehicle and storage medium | |
| EP4345421A2 (en) | Method for calibrating sensor parameters based on autonomous driving, apparatus, storage medium, and vehicle | |
| US20190100199A1 (en) | Course Prediction Method and Course Prediction Device | |
| CN111753605A (en) | Lane line positioning method and device, electronic equipment and readable medium | |
| CN111114544A (en) | Method and device for determining compensation lane line, movable equipment and storage medium | |
| CN113566817B (en) | Vehicle positioning method and device | |
| WO2024104418A1 (en) | Calibration method for lidar and calibration apparatus, storage medium, and terminal device | |
| CN110926405A (en) | ARV attitude measurement method based on monocular vision vanishing point detection | |
| CN112665506B (en) | Method, device, equipment and storage medium for detecting installation deviation of positioning device | |
| CN110850882A (en) | Charging pile positioning method and device of sweeping robot | |
| CN114812575A (en) | Correction parameter determining method and device, electronic equipment and storage medium | |
| CN107764273B (en) | Vehicle navigation positioning method and system | |
| CN110109165B (en) | Method and device for detecting abnormal points in driving track | |
| CN112284401B (en) | Data processing method and device | |
| CN109710594B (en) | Map data validity judging method and device and readable storage medium | |
| CN111457913B (en) | Vehicle navigation data fusion method, device and system | |
| CN106485917B (en) | A kind of method and apparatus for adjudicating vehicle to change lane | |
| CN112284402B (en) | Vehicle positioning method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |