CN110794392A - Vehicle positioning method and device, vehicle and storage medium - Google Patents
Vehicle positioning method and device, vehicle and storage medium Download PDFInfo
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- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- 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
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Abstract
The invention discloses a vehicle positioning method, a vehicle positioning device, a vehicle and a storage medium, wherein the method comprises the following steps: the method comprises the steps of acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle, determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and a pre-established high-precision map, wherein the high-precision map comprises a feature descriptor determined according to the point cloud data acquired by the millimeter wave radar, and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data. The method and the device have the advantages that the position of the vehicle in the high-precision map is determined according to the target point cloud data acquired by the millimeter wave radar, and compared with the prior art, the method and the device are low in cost, high in positioning precision and high in positioning reliability.
Description
Technical Field
The embodiment of the invention relates to the field of intelligent driving, in particular to a vehicle positioning method and device, a vehicle and a storage medium.
Background
The intelligent driving positioning technology can help the automobile to realize accurate positioning, and further realize unmanned driving through accurate path planning. Meanwhile, the intelligent driving positioning technology is also a part of the artificial intelligence technology and is the most important ring for realizing intelligent driving.
At present, a vehicle can be positioned by a Simultaneous positioning and Mapping (SLAM) technology based on a camera, and the main principle is to establish an environment model in a motion process and estimate the motion of the vehicle at the same time by the camera under the condition of no environment prior information.
However, the above method is too dependent on a camera, and when weather conditions are severe, positioning cannot be achieved, reliability is low, and positioning accuracy of the above method is also low.
Disclosure of Invention
The invention provides a vehicle positioning method, a vehicle positioning device, a vehicle and a storage medium, which aim to solve the technical problems of low reliability and low positioning precision of the conventional vehicle positioning mode.
In a first aspect, an embodiment of the present invention provides a vehicle positioning method, including:
acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle;
determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and the pre-established high-precision map; the high-precision map comprises a feature descriptor determined according to point cloud data acquired by a millimeter wave radar;
and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
In a second aspect, an embodiment of the present invention provides a vehicle positioning apparatus, including:
the system comprises an acquisition module, a data acquisition module and a data acquisition module, wherein the acquisition module is used for acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle;
the first determining module is used for determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and the pre-established high-precision map; the high-precision map comprises a feature descriptor determined according to point cloud data acquired by a millimeter wave radar;
and the second determination module is used for determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
In a third aspect, an embodiment of the present invention further provides a vehicle, including:
one or more processors;
a memory for storing one or more programs;
one or more millimeter wave radars;
when executed by the one or more processors, cause the one or more processors to implement the vehicle positioning method as provided in the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the vehicle positioning method provided in the first aspect.
The embodiment provides a vehicle positioning method, a vehicle positioning device, a vehicle and a storage medium, wherein the method comprises the following steps: the method comprises the steps of acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle, determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and a pre-established high-precision map, wherein the high-precision map comprises a feature descriptor determined according to the point cloud data acquired by the millimeter wave radar, and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data. The method and the device have the advantages that the position of the vehicle in the high-precision map is determined according to the target point cloud data acquired by the millimeter wave radar, and compared with the prior art, the method and the device are low in cost, high in positioning precision and high in positioning reliability.
Drawings
FIG. 1A is a schematic diagram of an application scenario of an embodiment of a vehicle positioning method provided by the present invention;
FIG. 1B is a schematic illustration of a vehicle provided by the present invention;
FIG. 2 is a schematic flow chart illustrating an embodiment of a vehicle positioning method provided by the present invention;
FIG. 3 is a schematic diagram of an unclosed high precision map constructed in an embodiment of a vehicle localization method;
FIG. 4A is a schematic diagram of point cloud data before surface normal estimation is performed in an embodiment of a vehicle localization method;
FIG. 4B is a diagram illustrating the results of surface normal estimation performed in an embodiment of a vehicle localization method;
FIG. 5A is a schematic diagram of an area of influence when determining a point feature histogram descriptor in an embodiment of a vehicle localization method;
FIG. 5B is a schematic diagram of a coordinate system when determining a point feature histogram descriptor in an embodiment of a vehicle localization method;
FIG. 6 is a schematic diagram of an area of influence when determining a fast point feature histogram descriptor in an embodiment of a vehicle localization method;
FIG. 7A is a schematic diagram of one implementation of a method embodiment of vehicle location to determine the location of a vehicle in a high accuracy map;
FIG. 7B is a schematic diagram of another implementation of determining a position of a vehicle in a high accuracy map in an embodiment of a vehicle location method;
FIG. 8 is a schematic structural diagram of an embodiment of a vehicle positioning device provided by the present invention;
fig. 9 is a schematic structural diagram of a vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1A is a schematic view of an application scenario of an embodiment of a vehicle positioning method provided by the present invention. FIG. 1B is a schematic view of a vehicle provided by the present invention. As shown in fig. 1B, the present invention provides a vehicle 10 on which a millimeter wave radar 11 is mounted. One millimeter wave radar 11 may be installed at the vehicle head, specifically, at a midpoint of the vehicle head in the left-right direction. Alternatively, two millimeter wave radars 11 are respectively installed at both ends in the left-right direction of the vehicle head, two millimeter wave radars 11 are respectively installed at the midpoint in the front-rear direction of both sides of the vehicle, two millimeter wave radars 11 are respectively installed at both ends in the left-right direction of the vehicle tail, and one millimeter wave radar 11 is installed at the midpoint in the left-right direction of the vehicle tail. As shown in fig. 1A, during the driving process of the vehicle, the millimeter wave radar mounted on the vehicle can be used to collect road condition information. In this embodiment, the millimeter wave radar may collect target point cloud data corresponding to the target point 13. In the field of intelligent driving, the vehicle can determine the position of the vehicle in a high-precision map according to target point cloud data acquired by a millimeter wave radar, and the vehicle can be positioned.
Fig. 2 is a schematic flow chart of an embodiment of a vehicle positioning method provided by the invention. The embodiment is suitable for a scene for positioning the vehicle in the field of intelligent driving. The present embodiment may be performed by a vehicle locating device, which may be implemented in software and/or hardware, which may be integrated into a vehicle. As shown in fig. 2, the vehicle positioning method provided in this embodiment includes the following steps:
step 201: and acquiring target point cloud data acquired by a millimeter wave radar arranged on the vehicle in the running process of the vehicle.
Specifically, the millimeter wave is an electromagnetic wave, which is composed of an alternating electric field and a magnetic field, and can propagate in vacuum, air, and free space. The frequency band is special, the frequency is higher than radio and lower than visible light and infrared, and the frequency range is 10 GHz-200 GHz. In this frequency band, the millimeter wave-related characteristics make it well suited for use in the automotive field. The millimeter wave radar frequency band in the vehicle-mounted field mainly comprises three sections: 24GHz band, 77GHz band, and 79GHz band. The 24GHz frequency band is mainly used for blind spot monitoring and lane change assistance of automobiles. The millimeter wave radar of the 24GHz frequency band can be installed in a rear bumper of a vehicle and used for monitoring whether the lanes on the two sides behind the vehicle have the vehicle or not and whether lane changing can be carried out or not. The 77 GHz-frequency-band millimeter wave radar is mainly used for being assembled on a front bumper of a vehicle, detecting the distance between the radar and the front vehicle and the speed of the front vehicle, and realizing the functions in the active safety fields of emergency braking, automatic following and the like. The 79GHz frequency band millimeter wave radar has very high resolution which can reach 5 cm. This resolution is very valuable in the field of smart driving, since smart driving cars distinguish between many delicate objects, such as pedestrians. For example, the performance parameters of a current millimeter wave radar may be as follows: the frequency is 76-77 GHz, the detection distance is 0.5-250 m, the visual angle is +/-15 degrees, the resolution is 0.5 m, the relative speed is-75- +60 m/s, and the speed resolution is 0.6 m/s. The millimeter-wave radar according to this embodiment may be a millimeter-wave radar in any of the frequency bands, which is not limited in this embodiment.
The millimeter wave radar sends out directional millimeter waves with corresponding wave bands through the transmitting antenna, and when the millimeter waves meet a target point, the millimeter waves are reflected back, and the reflected millimeter waves are received through the receiving antenna. And measuring the position of the target point according to the time difference between the millimeter wave receiving and sending, wherein the delay time td is 2R/C, wherein R is the distance between the target point and the vehicle, and C is the light speed. For the direction angle of the target point, the detection principle of the millimeter wave radar is as follows: after the millimeter waves are transmitted by the transmitting antenna of the millimeter wave radar and reflected by a target point, the receiving antenna parallel to the millimeter wave radar receives the reflected millimeter waves. By receiving the phase difference of the millimeter waves reflected by the same target point, the azimuth angle of the target point relative to the vehicle can be calculated.
In this embodiment, the vehicle positioning device may be specifically integrated in a vehicle controller of a vehicle. And in the running process of the vehicle, the millimeter wave radar arranged on the vehicle collects target point cloud data in real time. In one implementation, a vehicle locating device may actively acquire target point cloud data acquired by a millimeter wave radar. In another implementation mode, the millimeter wave radar actively sends the collected target point cloud data to the vehicle positioning device.
Optionally, the target point cloud data in this embodiment may include coordinate values of the collected target point in the vehicle coordinate system. The vehicle coordinate system in the present embodiment is a coordinate system established with a point on the vehicle as an origin. The vehicle coordinate system in the present embodiment may be a three-dimensional coordinate system or a polar coordinate system. More specifically, the origin of the vehicle coordinate system is the center point of the rear axle of the vehicle, the positive X-axis direction of the vehicle coordinate system is the left direction of the vehicle when the vehicle is facing forward, the positive Y-axis direction is the forward direction of the vehicle, and the positive Z-axis direction is the upward direction of the vehicle.
It should be noted that the target point cloud data may include coordinate values of the collected target points in the vehicle coordinate system.
In this embodiment, the target point corresponding to the target point cloud data acquired by the millimeter wave radar is a scattered point, rather than a determined point on an object. According to the implementation mode, on one hand, whether the target point is a point on a certain object is determined without a millimeter wave radar, so that the cost is reduced, and on the other hand, the compatibility is better.
Step 202: and determining the position of a target object corresponding to the target point cloud data in the high-precision map according to the target point cloud data and the pre-established high-precision map.
The high-precision map comprises a feature descriptor determined according to point cloud data acquired by the millimeter wave radar.
Specifically, in this embodiment, a high-precision map is established in advance, and specifically, point cloud data may be collected by a millimeter wave radar disposed on a collection vehicle. After the point cloud data are collected, the point cloud data are spliced to form a high-precision map. The specific process is as follows: (1) clustering the point cloud data to form a plurality of point cloud data sets; (2) selecting a standard from the two point cloud data sets according to the same key points, and extracting the key points; (3) respectively calculating feature descriptors of all the selected key points; (4) estimating the corresponding relation of the coordinate positions of the feature descriptors in the two point cloud data sets by combining the coordinate positions of the feature descriptors in the two point cloud data sets and taking the similarity of the features and the positions between the two point cloud data sets as a basis, and preliminarily estimating corresponding point pairs; (5) assuming the data is noisy, removing pairs of corresponding points of error that contribute to the splice; (6) and estimating rigid body transformation by using the residual correct corresponding relation to complete the splicing of the point cloud data set.
When a high-precision map based on a millimeter-wave radar is constructed, a Global Positioning System (GPS) is used to record a driving track of a collection vehicle. The point cloud data are spliced to form an initial high-precision map, and the running tracks of the vehicles need to be compared when the high-precision map is closed. Because the point cloud data have an angle error in the splicing process, the angle error is gradually increased along with the accumulation of the construction error of the high-precision map, and therefore the initial high-precision map needs to be corrected by means of the driving track recorded by the GPS, and the closing of the high-precision map is completed. FIG. 3 is a schematic diagram of an unclosed high-precision map constructed in an embodiment of a vehicle localization method. As shown in fig. 3, the dark line is the vehicle trajectory recorded by GPS and the light line is the initial high-precision map constructed from the point cloud data that is not closed.
Further, in order to improve the efficiency of subsequent vehicle positioning, a feature descriptor of the point cloud data is also included in the high-precision map. Size information of the object may also be included in the high-precision map.
Optionally, the specific implementation process of step 202 may be: determining key point cloud data from the target point cloud data; determining a feature descriptor of the key point cloud data according to a point cloud data feature descriptor extraction algorithm; and determining the position of a target object corresponding to the target point cloud data in the high-precision map according to the feature descriptors of the key point cloud data and the feature descriptors in the high-precision map.
Key point cloud data may be determined from the target point cloud data according to key point extraction criteria. The point cloud data feature descriptor extraction algorithm in the embodiment includes the following four algorithms: estimating a surface normal of the Point cloud, a Point Feature Histogram (PFH) descriptor, a Fast Point Feature Histogram (FPFH), and a Viewpoint Feature Histogram (VFH) descriptor. These four algorithms are described in detail below.
(1) Estimating the surface normal of the point cloud:
the surface normal is an important property of the surface of a geometric body, and for a known surface of a geometric body, the direction of the normal to a point on the surface can be inferred from the vector perpendicular to the surface of the point. The point cloud data set is represented as a set of fixed point samples on the surface of the real object, and the surface normal can be approximated directly from the key point cloud data in this embodiment.
The problem of determining a point normal to a surface approximates the problem of estimating a tangent plane normal to the surface, and so the transformation becomes a least squares plane fitting estimation problem. The solution to estimate the surface normal therefore becomes to analyze the eigenvectors and eigenvalues of a covariance matrix created from the neighboring elements of the query point. The query point here refers to key point cloud data. More specifically, for each point Pi, the corresponding covariance matrix C has the following formula:
where k is the number of points adjacent to point Pi,
representing the three-dimensional centroid, λ, of the nearest neighbor elementjIs the jth eigenvalue of the covariance matrix,
is the jth feature vector.
Fig. 4A is a schematic diagram of point cloud data before surface normal estimation is performed in an embodiment of a vehicle positioning method.
Fig. 4B is a schematic diagram illustrating a result of surface normal estimation performed in the embodiment of the vehicle positioning method.
(2) PFH descriptor:
the PFH is related to the three-dimensional data of the coordinate axes and the surface normal, and thus can be better used to describe the three-dimensional features of the point cloud data set. The PFH calculation mode describes the k neighborhood geometric attributes of the query point by parameterizing the spatial difference between the query point and the neighborhood point and forming a multi-dimensional histogram. The high-dimensional hyperspace where the histogram is located provides a measurable information space for feature representation, and the feature space has invariance to the 6-dimensional postures of the point cloud corresponding to the curved surface and has noise levels in different sampling densities or neighborhoodsThe method has robustness. The PFH representation is based on the relationship between the query point and its k-neighborhood and their estimated normals, and in short, it takes into account all the interactions between the estimated normal directions in an attempt to capture the best sample surface variations to describe the geometric features of the sample. The synthesis of the feature hyperspace is therefore dependent on the quality of the surface normal estimate for each point. FIG. 5A is a schematic diagram of an area of influence when determining a point feature histogram descriptor in an embodiment of a vehicle localization method. As shown in FIG. 5A, a query point (P) is shownq) The area of influence of PFH calculation of (1), PqIs located at the middle position of the sphere with the radius r and PqAll k neighbors of (i.e. with point P)qLess than all points of radius r) are all interconnected in a network. The final PFH descriptor is a histogram obtained by computing the relationship between all two points in the neighborhood.
FIG. 5B is a diagram of a coordinate system for determining a point feature histogram descriptor in an embodiment of a vehicle localization method. As shown in FIG. 5B, to calculate two points PiAnd PjAnd normal n corresponding to themiAnd njA fixed local coordinate system is defined at one of the points.
u＝ns
u＝ns
w＝u×v
Using the u-v-w coordinate system in FIG. 5B, normal nsAnd ntThe deviation between can be represented by a set of angles as follows:
α＝v·nt
θ＝arctan(w·nt,u·nt)
wherein d is the Euclidean distance between two points Ps and Pt, and d | | | Pt–Ps||2. Four sets of values are calculated for each pair of points in k neighborhood.
Creating the final PFH descriptor for the query point, all the quadruples will be put into the histogram in some statistical way, this procedure first divides each range of feature values into b sub-bins and counts the number of points falling in each sub-bin, since three quarters of the features are measured by the angle between the normals in the above, their parameter values can be very easily normalized to the same bin on a triangulated circle. An example of a statistic is: dividing each feature interval into equal number of equal divisions, for which b is created in a fully associative space4Histogram of individual bins. In this space, an increase in the statistical number of a certain bin in a histogram corresponds to 4 feature values of a point.
In summary, in the process of calculating the PFH descriptor, the actual calculation process for each point P in the point cloud data P is as follows: obtaining nearest neighbor elements of the p points; for each pair of points in the neighborhood, calculating three angle characteristic parameter values of the points; all results are counted into one output histogram.
(3) FPFH descriptor:
given that there are n points in the point cloud data P, k is the number of neighborhoods considered when computing the feature vector for each point P in the point cloud P in its theoretical computation of PFH. In practical applications, the calculation of PFH of dense point clouds requires a large amount of computational resources. Therefore, after the PFH calculation mode is simplified, the FPFH can be obtained to describe the characteristics of the point cloud.
To simplify the feature computation of the histogram, we perform the following process:
the first step is as follows: for each query Point, calculating a tuple between the Point and its neighborhood points, called Simplified Point Feature Histograms (SPFH);
second, the k neighborhood of each point is re-determined, and the final histogram (called FPFH) is computed using the neighboring SPFH values, as follows:
in the above formula, the weight wkIn some given metric spaces, a query point P is representedqAnd its neighboring points PkAnd thus can be used to assess a pair of points (P)q,Pk). FIG. 6 is a schematic diagram of an influence area when a fast point feature histogram descriptor is determined in an embodiment of a vehicle positioning method.
Thus, for a known query point, the algorithm first uses only PqAnd its neighborhood points (illustrated by line 31 in fig. 6) to estimate its SPFH value, this is clearly less than the standard calculation of PFH for interconnection between neighborhood points. This calculation is performed for all points in the point cloud dataset to obtain SPFH, which is then used for its neighboring points PkSPFH value and P ofqThe SPFH value of the point is re-weighted to obtain PqFinal FPFH value of the point.
(4) VFH descriptor:
the VFH descriptor is mainly applied to the problems of point cloud cluster identification and six-degree-of-freedom pose estimation. The VFH descriptor is derived from the FPFH descriptor. Features are constructed for application to object recognition problems and pose estimation by two calculations: (1) expanding the FPFH to make the FPFH utilize the whole point cloud object to carry out calculation estimation, and taking the point pairs between the center point of the object and all other points on the surface of the object as a calculation unit when calculating the FPFH; and (2) adding additional statistical information between the viewpoint direction and each point estimation normal, and directly integrating the viewpoint direction variable into the relative normal angle calculation in the FPFH calculation.
The viewpoint-dependent feature components are calculated by counting histograms of angles between the viewpoint direction and each normal. The view angle of each normal is not the same because the view angle of the normal has variability in scale transformation, which refers here to the view direction after translating the view to the query point and the angle between each normal. The second set of feature components are the three angles taught in PFH, now measured between the viewpoint direction at the center point and each surface normal. The uniqueness of VFH is mainly manifested in two ways: a view direction dependent component and a surface shape describing component containing the extended FPFH.
After the feature descriptor of the key point cloud data is calculated in any one of the four ways, the position of the target object corresponding to the target point cloud data in the high-precision map is determined according to the feature descriptor of the key point cloud data and the feature descriptor in the high-precision map. One point cloud dataset is accurately registered with another point cloud dataset by applying an estimated 4 x 4 rigid transformation matrix representing translation and rotation. The specific process can be as follows: estimating the corresponding relation of the coordinate positions of the feature descriptors in the two data sets by combining the coordinate positions of the feature descriptors and the coordinate positions of the feature descriptors; assuming the data is noisy, removing the corresponding pairs of points of error that contribute to the registration; and estimating rigid body transformation by using the residual correct corresponding relation to complete registration. The most important thing in the whole registration process is the extraction of key points and the feature description of the key points. So as to ensure the accuracy and efficiency of the corresponding estimation, and thus, the correctness of the rigid body transformation matrix estimation in the subsequent process can be ensured.
It should be noted that the target object corresponding to the target point cloud data in this embodiment may be a target object corresponding to one or several target points in the target points included in the point cloud data. Illustratively, the target object may be a road edge or a curb rail or the like.
Alternatively, a coordinate system may be established with a certain point on the high-precision map as an origin, and the position of the target object in the high-precision map may be shown in the form of coordinates.
Step 203: and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
Specifically, after the position of the target object in the high-precision map is determined, the position of the vehicle in the high-precision map can be determined according to the position of the target object in the high-precision map and the target point cloud data, so that the vehicle can be positioned.
In one implementation, the coordinate values of the target object in the vehicle coordinate system may be determined according to the target point cloud data; and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system. In this implementation, the origin of the vehicle coordinate system is the rear axle center point of the vehicle, and the positive X-axis direction of the vehicle coordinate system is the left direction of the vehicle when the vehicle is facing forward, the positive Y-axis direction is the forward direction of the vehicle, and the positive Z-axis direction is the upward direction of the vehicle.
As described in step 202, the target object corresponding to the target point cloud data may be a target object corresponding to one or more target points in the target points included in the point cloud data, that is, some target points included in the target point cloud data do not belong to the points corresponding to the target object. In this embodiment, when determining the coordinate value of the target object in the vehicle coordinate system according to the target point cloud data, an average value or a value of a center point of the target point cloud data corresponding to the target point belonging to the target object may be used as the coordinate value of the target object in the vehicle coordinate system. Then, according to the coordinate value of the target object in the vehicle coordinate system, determining the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle; and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map, the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle.
FIG. 7A is a schematic diagram of one implementation of determining a position of a vehicle in a high-precision map in an embodiment of a vehicle location method. As shown in fig. 7A, according to the coordinate values of the target object in the vehicle coordinate system, the distance between the target object and the vehicle and the angle between the target object and the vehicle advancing direction, i.e., the Y-axis, can be determined. In this embodiment, the vehicle may be represented by the origin of the vehicle coordinate system. The position of the target object in the high-precision map, the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle are known, and the position of the vehicle in the high-precision map can be calculated according to a triangular geometric principle.
In one implementation, when there are a plurality of target objects corresponding to the target point cloud data, in the above implementation, the specific process of determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system may be: determining a plurality of initial positions of the vehicle in the high-precision map according to the position of each target object in the high-precision map and the coordinate value of the corresponding target object in the vehicle coordinate system; determining the sum of the distances between each initial position and other initial positions; the minimum distance and the corresponding initial position are determined as the position of the vehicle in the high-precision map.
FIG. 7B is a schematic diagram of another implementation of determining the position of a vehicle in a high-precision map in an embodiment of a vehicle location method. As shown in fig. 7B, the initial position set (P1, P2, P3 …. Pn) of the vehicle can be correspondingly calculated based on the target object 1, the target object 2, the target object 3, … …, and the target object n. Then, the distance sum between each initial position and other initial positions is determined, and the minimum distance sum corresponding to the initial position is determined as the position of the vehicle in the high-precision map. This way the accuracy of the vehicle positioning can be improved.
Further, in order to improve the efficiency of vehicle positioning, the average position of the vehicle in the high-precision map may be determined from a plurality of initial positions; determining the sum of the distances between each initial position and other initial positions, wherein the distance between each initial position and the average position is smaller than a preset threshold value; the minimum distance and the corresponding initial position are determined as the position of the vehicle in the high-precision map. In the implementation mode, when the distance sum is determined, only the distance sum between the initial position and the other initial positions is determined, wherein the distance sum is less than the preset threshold, compared with the method of computing the distance sum in an enumeration mode, the computation amount is reduced, and the efficiency of the algorithm is improved.
According to the vehicle positioning method provided by the embodiment, the millimeter wave radar is used for collecting the target point cloud data to position the vehicle, and compared with the laser radar, the base station positioning, the SLAM and other modes, the vehicle positioning method has the advantages of low cost and high positioning precision, can also identify black objects, can be used in severe weather such as cloud and fog, and is higher in positioning reliability.
The vehicle positioning method provided by the embodiment comprises the following steps: the method comprises the steps of acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle, determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and a pre-established high-precision map, wherein the high-precision map comprises a feature descriptor determined according to the point cloud data acquired by the millimeter wave radar, and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data. The method and the device have the advantages that the position of the vehicle in the high-precision map is determined according to the target point cloud data acquired by the millimeter wave radar, and compared with the prior art, the method and the device are low in cost, high in positioning precision and high in positioning reliability.
Fig. 8 is a schematic structural diagram of an embodiment of a vehicle positioning device provided by the invention. As shown in fig. 8, the present embodiment provides a vehicle positioning device including: an acquisition module 81, a first determination module 82, and a second determination module 83.
The acquisition module 81 is configured to acquire target point cloud data acquired by a millimeter wave radar arranged on a vehicle during a vehicle driving process.
And the first determining module 82 is configured to determine, according to the target point cloud data and a pre-established high-precision map, a position of a target object corresponding to the target point cloud data in the high-precision map.
The high-precision map comprises a feature descriptor determined according to point cloud data acquired by the millimeter wave radar.
Optionally, the first determining module 82 is specifically configured to: determining key point cloud data from the target point cloud data; determining a feature descriptor of the key point cloud data according to a point cloud data feature descriptor extraction algorithm; and determining the position of a target object corresponding to the target point cloud data in the high-precision map according to the feature descriptors of the key point cloud data and the feature descriptors in the high-precision map.
And the second determining module 83 is used for determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
Optionally, the second determining module 83 specifically includes: a first determination submodule and a second determination submodule. And the first determining submodule is used for determining the coordinate value of the target object in the vehicle coordinate system according to the target point cloud data. The vehicle coordinate system is a coordinate system established by taking one point on the vehicle as an origin. And the second determining submodule is used for determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system.
In one implementation, the origin of the vehicle coordinate system is a rear axle center point of the vehicle, the positive X-axis direction of the vehicle coordinate system is a left side direction of the vehicle when the vehicle faces forward, the positive Y-axis direction is a forward direction of the vehicle, and the positive Z-axis direction is an upward direction of the vehicle. The second determination submodule is specifically configured to: determining the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle according to the coordinate value of the target object in the vehicle coordinate system; and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map, the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle.
Optionally, when a plurality of target objects correspond to the target point cloud data, the second determining sub-module specifically includes: a first determination unit, a second determination unit, and a third determination unit. And the first determining unit is used for determining a plurality of initial positions of the vehicle in the high-precision map according to the position of each target object in the high-precision map and the coordinate value of the corresponding target object in the vehicle coordinate system. And the second determining unit is used for determining the sum of the distances between each initial position and other initial positions. And a third determining unit for determining the minimum distance and the corresponding initial position as the position of the vehicle in the high-precision map. Still further, based on this implementation, the vehicle positioning apparatus may further include: and a fourth determination unit for determining an average position of the vehicle in the high-precision map based on the plurality of initial positions. Correspondingly, the second determining unit is specifically configured to: and determining the sum of the distances between each initial position and other initial positions, wherein the distance between each initial position and the average position is smaller than a preset threshold value.
The vehicle positioning device provided by the embodiment of the invention can execute the vehicle positioning method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 9 is a schematic structural diagram of a vehicle according to the present invention. As shown in fig. 9, the vehicle includes a processor 70, a memory 71, and a millimeter wave radar 72. The number of the processors 70 in the vehicle may be one or more, and the number of the millimeter wave radars 72 may be one or more, and fig. 9 exemplifies one processor 70 and one millimeter wave radar 72; the processor 70 and memory 71 of the vehicle may be connected by a bus or other means, as exemplified by the bus connection in fig. 9.
The memory 71 serves as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions and modules corresponding to the vehicle positioning method in the embodiment of the present invention (for example, the acquisition module 81, the first determination module 82, and the second determination module 83 in the vehicle positioning apparatus). The processor 70 executes various functional applications and data processing of the vehicle by executing software programs, instructions and modules stored in the memory 71, so as to implement the vehicle positioning method described above.
The memory 71 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; the storage data area may store data created according to the use of the vehicle, and the like. Further, the memory 71 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 71 may further include memory located remotely from the processor 70, which may be connected to the vehicle over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The present invention also provides a storage medium containing computer-executable instructions which, when executed by a computer processor, are operable to perform a vehicle localization method, the method comprising:
acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle;
determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and the pre-established high-precision map; the high-precision map comprises a feature descriptor determined according to point cloud data acquired by a millimeter wave radar;
and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the vehicle positioning method provided by any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer device (which may be a personal computer, a vehicle, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the vehicle positioning device, the included units and modules are merely divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A vehicle positioning method, characterized by comprising:
acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle;
determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and the pre-established high-precision map; the high-precision map comprises a feature descriptor determined according to point cloud data acquired by a millimeter wave radar;
and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
2. The method of claim 1, wherein determining the location of the vehicle in the high accuracy map from the location of the target object in the high accuracy map and the target point cloud data comprises:
determining the coordinate value of the target object in a vehicle coordinate system according to the target point cloud data; the vehicle coordinate system is a coordinate system established by taking one point on the vehicle as an origin;
and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system.
3. The method of claim 2, wherein the origin of the vehicle coordinate system is a rear axle center point of the vehicle, the positive X-axis direction of the vehicle coordinate system is a left side direction of the vehicle when the vehicle is facing forward, the positive Y-axis direction is a forward direction of the vehicle, and the positive Z-axis direction is an upward direction of the vehicle;
the determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system comprises:
according to the coordinate value of the target object in the vehicle coordinate system, determining the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle;
and determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map, the distance between the target object and the vehicle and the included angle between the target object and the advancing direction of the vehicle.
4. The method according to claim 2, wherein when a plurality of target objects correspond to the target point cloud data, the determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system comprises:
determining a plurality of initial positions of the vehicle in the high-precision map according to the position of each target object in the high-precision map and the coordinate value of the corresponding target object in the vehicle coordinate system;
determining the sum of the distances between each initial position and other initial positions;
and determining the minimum distance and the corresponding initial position as the position of the vehicle in the high-precision map.
5. The method of claim 4, wherein after determining the plurality of initial positions of the vehicle in the high-precision map, the method further comprises:
determining an average position of the vehicle in the high-precision map according to the plurality of initial positions;
accordingly, the determining the sum of the distances between each of the initial positions and the other initial positions comprises:
and determining the sum of the distances between each initial position and other initial positions, wherein the distance between each initial position and the average position is smaller than a preset threshold value.
6. The method according to any one of claims 1 to 5, wherein the determining the position of the target object corresponding to the target point cloud data in the high-precision map according to the target point cloud data and a pre-established high-precision map comprises:
determining key point cloud data from the target point cloud data;
determining a feature descriptor of the key point cloud data according to a point cloud data feature descriptor extraction algorithm;
and determining the position of a target object corresponding to the target point cloud data in the high-precision map according to the feature descriptor of the key point cloud data and the feature descriptor in the high-precision map.
7. A vehicle positioning device, comprising:
the system comprises an acquisition module, a data acquisition module and a data acquisition module, wherein the acquisition module is used for acquiring target point cloud data acquired by a millimeter wave radar arranged on a vehicle in the driving process of the vehicle;
the first determining module is used for determining the position of a target object corresponding to the target point cloud data in a high-precision map according to the target point cloud data and the pre-established high-precision map; the high-precision map comprises a feature descriptor determined according to point cloud data acquired by a millimeter wave radar;
and the second determination module is used for determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the target point cloud data.
8. The apparatus of claim 7, wherein the second determining module specifically comprises:
the first determining submodule is used for determining the coordinate value of the target object in a vehicle coordinate system according to the target point cloud data; the vehicle coordinate system is a coordinate system established by taking one point on the vehicle as an origin;
and the second determining submodule is used for determining the position of the vehicle in the high-precision map according to the position of the target object in the high-precision map and the coordinate value of the target object in the vehicle coordinate system.
9. A vehicle, characterized in that the vehicle comprises:
one or more processors;
a memory for storing one or more programs;
one or more millimeter wave radars;
when executed by the one or more processors, cause the one or more processors to implement the vehicle positioning method of any of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a vehicle localization method according to any one of claims 1-6.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111505662A (en) * | 2020-04-29 | 2020-08-07 | 北京理工大学 | Unmanned vehicle positioning method and system |
| CN111638528A (en) * | 2020-05-26 | 2020-09-08 | 北京百度网讯科技有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN111812658A (en) * | 2020-07-09 | 2020-10-23 | 北京京东乾石科技有限公司 | Position determination method, device, system and computer readable storage medium |
| CN112287557A (en) * | 2020-11-09 | 2021-01-29 | 东风汽车集团有限公司 | Radar point cloud data loop playback method and system for assisting driving simulation test |
| CN113419235A (en) * | 2021-05-28 | 2021-09-21 | 同济大学 | Unmanned aerial vehicle positioning method based on millimeter wave radar |
| CN114252883A (en) * | 2020-09-24 | 2022-03-29 | 北京万集科技股份有限公司 | Target detection method, apparatus, computer device and medium |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102928816A (en) * | 2012-11-07 | 2013-02-13 | 东南大学 | High-reliably integrated positioning method for vehicles in tunnel environment |
| US9140792B2 (en) * | 2011-06-01 | 2015-09-22 | GM Global Technology Operations LLC | System and method for sensor based environmental model construction |
| WO2017150059A1 (en) * | 2016-03-02 | 2017-09-08 | 三菱電機株式会社 | Autonomous travel assistance device, roadside device, and autonomous travel assistance system |
| US20170314926A1 (en) * | 2016-04-28 | 2017-11-02 | Rogerson Aircraft Corporation | System and method for effectuating presentation of a terrain around a vehicle on a display in the vehicle |
| US20180306922A1 (en) * | 2017-04-20 | 2018-10-25 | Baidu Online Network Technology (Beijing) Co., Ltd | Method and apparatus for positioning vehicle |
| CN108983248A (en) * | 2018-06-26 | 2018-12-11 | 长安大学 | It is a kind of that vehicle localization method is joined based on the net of 3D laser radar and V2X |
| CN109031269A (en) * | 2018-06-08 | 2018-12-18 | 上海西井信息科技有限公司 | Localization method, system, equipment and storage medium based on millimetre-wave radar |
| CN109425365A (en) * | 2017-08-23 | 2019-03-05 | 腾讯科技（深圳）有限公司 | Method, apparatus, equipment and the storage medium of Laser Scanning Equipment calibration |
| CN109459750A (en) * | 2018-10-19 | 2019-03-12 | 吉林大学 | A kind of more wireless vehicle trackings in front that millimetre-wave radar is merged with deep learning vision |
| WO2019099802A1 (en) * | 2017-11-17 | 2019-05-23 | DeepMap Inc. | Iterative closest point process based on lidar with integrated motion estimation for high definitions maps |
| US20190171212A1 (en) * | 2017-11-24 | 2019-06-06 | Baidu Online Network Technology (Beijing) Co., Ltd | Method and apparatus for outputting information of autonomous vehicle |
| CN109870689A (en) * | 2019-01-08 | 2019-06-11 | 武汉中海庭数据技术有限公司 | Millimetre-wave radar and the matched lane grade localization method of high-precision map vector and system |
| US20190195998A1 (en) * | 2017-12-22 | 2019-06-27 | Waymo Llc | Radar Based Three Dimensional Point Cloud for Autonomous Vehicles |
| CN110044371A (en) * | 2018-01-16 | 2019-07-23 | 华为技术有限公司 | A kind of method and vehicle locating device of vehicle location |
| CN110084272A (en) * | 2019-03-26 | 2019-08-02 | 哈尔滨工业大学（深圳） | A kind of cluster map creating method and based on cluster map and the matched method for relocating of location expression |
| CN110148144A (en) * | 2018-08-27 | 2019-08-20 | 腾讯大地通途（北京）科技有限公司 | Dividing method and device, storage medium, the electronic device of point cloud data |
| CN110208739A (en) * | 2019-05-29 | 2019-09-06 | 北京百度网讯科技有限公司 | Assist method, apparatus, equipment and the computer readable storage medium of vehicle location |
-
2019
- 2019-10-15 CN CN201910978766.XA patent/CN110794392B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9140792B2 (en) * | 2011-06-01 | 2015-09-22 | GM Global Technology Operations LLC | System and method for sensor based environmental model construction |
| CN102928816A (en) * | 2012-11-07 | 2013-02-13 | 东南大学 | High-reliably integrated positioning method for vehicles in tunnel environment |
| WO2017150059A1 (en) * | 2016-03-02 | 2017-09-08 | 三菱電機株式会社 | Autonomous travel assistance device, roadside device, and autonomous travel assistance system |
| US20170314926A1 (en) * | 2016-04-28 | 2017-11-02 | Rogerson Aircraft Corporation | System and method for effectuating presentation of a terrain around a vehicle on a display in the vehicle |
| US20180306922A1 (en) * | 2017-04-20 | 2018-10-25 | Baidu Online Network Technology (Beijing) Co., Ltd | Method and apparatus for positioning vehicle |
| CN108732582A (en) * | 2017-04-20 | 2018-11-02 | 百度在线网络技术（北京）有限公司 | Vehicle positioning method and device |
| CN109425365A (en) * | 2017-08-23 | 2019-03-05 | 腾讯科技（深圳）有限公司 | Method, apparatus, equipment and the storage medium of Laser Scanning Equipment calibration |
| WO2019099802A1 (en) * | 2017-11-17 | 2019-05-23 | DeepMap Inc. | Iterative closest point process based on lidar with integrated motion estimation for high definitions maps |
| US20190171212A1 (en) * | 2017-11-24 | 2019-06-06 | Baidu Online Network Technology (Beijing) Co., Ltd | Method and apparatus for outputting information of autonomous vehicle |
| US20190195998A1 (en) * | 2017-12-22 | 2019-06-27 | Waymo Llc | Radar Based Three Dimensional Point Cloud for Autonomous Vehicles |
| CN110044371A (en) * | 2018-01-16 | 2019-07-23 | 华为技术有限公司 | A kind of method and vehicle locating device of vehicle location |
| CN109031269A (en) * | 2018-06-08 | 2018-12-18 | 上海西井信息科技有限公司 | Localization method, system, equipment and storage medium based on millimetre-wave radar |
| CN108983248A (en) * | 2018-06-26 | 2018-12-11 | 长安大学 | It is a kind of that vehicle localization method is joined based on the net of 3D laser radar and V2X |
| CN110148144A (en) * | 2018-08-27 | 2019-08-20 | 腾讯大地通途（北京）科技有限公司 | Dividing method and device, storage medium, the electronic device of point cloud data |
| CN109459750A (en) * | 2018-10-19 | 2019-03-12 | 吉林大学 | A kind of more wireless vehicle trackings in front that millimetre-wave radar is merged with deep learning vision |
| CN109870689A (en) * | 2019-01-08 | 2019-06-11 | 武汉中海庭数据技术有限公司 | Millimetre-wave radar and the matched lane grade localization method of high-precision map vector and system |
| CN110084272A (en) * | 2019-03-26 | 2019-08-02 | 哈尔滨工业大学（深圳） | A kind of cluster map creating method and based on cluster map and the matched method for relocating of location expression |
| CN110208739A (en) * | 2019-05-29 | 2019-09-06 | 北京百度网讯科技有限公司 | Assist method, apparatus, equipment and the computer readable storage medium of vehicle location |
Non-Patent Citations (3)
| Title |
|---|
| 张会平 著: "《基于可视化技术的知识转化研究》", 电子科技大学出版社, pages: 107 - 108 * |
| 王仁礼, 陈天泽, 王冬红: "智能型地图匹配综合算法的研究", 计算机辅助设计与图形学学报, no. 11 * |
| 王庆闪等: "基于NDT与ICP结合的点云配准算法", 计算机工程与应用, pages 107 - 108 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111505662A (en) * | 2020-04-29 | 2020-08-07 | 北京理工大学 | Unmanned vehicle positioning method and system |
| CN111505662B (en) * | 2020-04-29 | 2021-03-23 | 北京理工大学 | Unmanned vehicle positioning method and system |
| CN111638528A (en) * | 2020-05-26 | 2020-09-08 | 北京百度网讯科技有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN111812658A (en) * | 2020-07-09 | 2020-10-23 | 北京京东乾石科技有限公司 | Position determination method, device, system and computer readable storage medium |
| CN111812658B (en) * | 2020-07-09 | 2021-11-02 | 北京京东乾石科技有限公司 | Position determination method, device, system and computer readable storage medium |
| CN114252883A (en) * | 2020-09-24 | 2022-03-29 | 北京万集科技股份有限公司 | Target detection method, apparatus, computer device and medium |
| CN114252883B (en) * | 2020-09-24 | 2022-08-23 | 北京万集科技股份有限公司 | Target detection method, apparatus, computer device and medium |
| CN112287557A (en) * | 2020-11-09 | 2021-01-29 | 东风汽车集团有限公司 | Radar point cloud data loop playback method and system for assisting driving simulation test |
| CN112287557B (en) * | 2020-11-09 | 2023-04-07 | 东风汽车集团有限公司 | Radar point cloud data loop playback method and system for assisting driving simulation test |
| CN113419235A (en) * | 2021-05-28 | 2021-09-21 | 同济大学 | Unmanned aerial vehicle positioning method based on millimeter wave radar |
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