CN108225341B - Vehicle positioning method - Google Patents

Vehicle positioning method Download PDF

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CN108225341B
CN108225341B CN201611154085.4A CN201611154085A CN108225341B CN 108225341 B CN108225341 B CN 108225341B CN 201611154085 A CN201611154085 A CN 201611154085A CN 108225341 B CN108225341 B CN 108225341B
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point cloud
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particles
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CN108225341A (en
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殷鹏
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FAFA Automobile (China) Co., Ltd.
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Fafa Automobile China Co ltd
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    • 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
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Abstract

The embodiment of the invention discloses a vehicle positioning method, which comprises the following steps: and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud. The method and the device for locating the local point cloud have the advantages that the local point cloud obtained by the vehicle in real time and the map point cloud stored in advance are used for locating, a point cloud locating result is obtained, and the resolution ratio of the local point cloud obtained by the vehicle in real time is lower than that of the map point cloud. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.

Description

Vehicle positioning method
Technical Field
The invention relates to the technical field of automobiles, in particular to a vehicle positioning method.
Background
With the development of automatic driving, the automatic driving system gradually goes into people's daily life from laboratory's laboratory, but simultaneously the environment that outdoor environment is complicated and changeable presents a series of new problems for automatic driving's environmental perception, location, navigation. Among them, the problems of high buildings, magnetic field influence, etc. in urban environments make the positioning algorithm depending on GPS positioning unable to provide stable and accurate positioning results.
For this problem, the prior art adopts an online optimization estimation algorithm (ORB _ SLAM, LSD _ SLAM) based on a visual odometer, however, this method is relatively heavily dependent on lighting conditions, and cannot ensure long-term location estimation. Therefore, the prior art proposes a fusion localization method based on Laser Odometer (LOAM) and visual laser odometer (V-LOAM), and keeps the status-of-art odometer localization result on the Kitti data set all the time.
However, in the process of implementing the invention, the inventor finds that the core of the algorithm in the prior art is to use frame-to-frame matching (Registration), a laser device with lower resolution (such as Velodyne16E) can only provide sparse point cloud information, and the accuracy of frame-to-frame matching is low, so that the prior art is often required to rely on a laser device with high accuracy (Velodyne 64E).
Disclosure of Invention
Based on this, it is necessary to provide a vehicle positioning method for solving the technical problem that the vehicle positioning method in the prior art needs to use a high-precision laser device.
The embodiment of the invention provides a vehicle positioning method, which comprises the following steps:
and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud.
Further, the positioning is performed by using the local point cloud obtained by the vehicle in real time and the map point cloud stored in advance to obtain a point cloud positioning result, which includes:
uniformly adjusting the resolution ratios of map point clouds stored in advance and local point clouds obtained by a vehicle in real time to be preset resolution ratios, wherein the preset resolution ratios are lower than the initial resolution ratios of the local point cloud information;
performing iterative matching until an iterative exit condition is satisfied:
randomly selecting N particles from a sampling range of the map point cloud as matching particles, carrying out ICP (inductively coupled plasma) matching on each matching particle and the particles of the local point cloud to obtain a matching estimation coefficient of each matching particle, and calculating according to the projection area of the map point cloud on the ground plane and the resolution of the current map point cloud to obtain N;
according to the matching estimation coefficient, carrying out particle filtering on the matched particles to obtain stable effective matched particles;
updating the map point cloud according to the effective matching particles and reducing the sampling range;
improving the resolution of the map point cloud and the local point cloud, if the iteration exit condition is met, exiting the iteration matching, and otherwise, executing the next iteration matching;
and if the iterative matching is exited, determining a point cloud positioning result according to the effective matching particles when the iterative matching is exited.
Furthermore, the step of uniformly adjusting the resolution of the map point cloud stored in advance and the local point cloud acquired by the vehicle in real time to a preset resolution includes:
converting a pre-stored map point cloud into an octree structure to obtain a map octree structure, and converting a local point cloud obtained by a vehicle in real time into the octree structure to obtain the local octree structure;
and adjusting the map octree structure and the local octree structure to the level corresponding to the preset resolution.
Still further, the obtaining of stable effective matching particles by particle filtering the matching particles according to the matching estimation coefficients includes:
calculating an observation probability function, a state transfer function and a point cloud density distribution function according to the matching estimation coefficient;
carrying out particle filtering according to an observation probability function, a state transfer function and a point cloud density distribution function to obtain the effective particle number Neff；
For the ith matched particle in the kth iteration of particle filtering, the matching estimation coefficient is
Figure BDA0001180328340000031
Figure BDA0001180328340000032
Wherein the content of the first and second substances,
Figure BDA0001180328340000033
for the map point cloud used in the kth iteration of particle filtering, M is the number of matching particles included in the map point cloud used in the kth iteration of particle filtering,
Figure BDA0001180328340000034
for the rotation-translation matrix, p, of the ith matched particle in the kth iteration of the particle filteringpFor the p-th particle in the local point cloud, qqFor the q-th matching particle in the map point cloud, and ppAnd q isqCorresponding;
corresponding observation probability function is
Figure BDA0001180328340000035
The corresponding state transfer function is
Figure BDA0001180328340000036
Wherein
Figure BDA0001180328340000037
To match the position of particle i in the kth iteration of particle filtering,
Figure BDA0001180328340000038
for the rotational-translation matrix of the ith matched particle in the (k-1) th iterative matching,
Figure BDA0001180328340000039
estimating coefficients for the matching of the ith matching particle in the k-1 th iteration matching;
corresponding point cloud density distribution function of
Figure BDA0001180328340000041
Figure BDA0001180328340000042
Wherein, Node (x)k (i)) Denoted as the parent node of the ith matched particle in the kth iteration of particle filtering.
Still further, the iteration exit condition is:
the average efficiency of the effective matching particles is smaller than a preset efficiency threshold, wherein the average efficiency of the effective matching particles is calculated in the following way:
sorting according to the matching efficiency index of each matching particle, and sorting the top N of the resultseffTaking the matched particles as effective matched particles, and calculating to obtain the average efficiency of the effective matched particles, wherein the matching efficiency index corresponding to the ith matched particle in the kth iteration of the particle filter is
Figure BDA0001180328340000043
Figure BDA0001180328340000044
Wherein t isk (i)The computation time for the ith matched particle at the kth iteration of the particle filter.
Further, the updating the map point cloud according to the valid matching particles and narrowing the sampling range includes:
calculating the clustering center and the clustering radius of the effective matching particles;
taking the clustering center as a center, and taking a preset multiple of the clustering radius as a search range;
combining the particles in the search range in the map point cloud into a search map point cloud, and updating the map point cloud into the search map point cloud.
Further, determining a point cloud positioning result according to the valid matching particles when the iterative matching is exited comprises:
and taking the clustering center of the effective matching particles when the iterative matching is exited as a point cloud positioning result.
Still further, the method further comprises:
and fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
Still further, the obtaining a fusion positioning result by fusing the point cloud positioning result and the navigation positioning result includes:
and fusing the point cloud positioning result and the navigation positioning result through extended Kalman filtering to obtain a fusion positioning result.
An embodiment of the present invention provides a vehicle positioning apparatus, including:
a point cloud localization module to: and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud.
Further, the point cloud positioning module is specifically configured to:
uniformly adjusting the resolution ratios of map point clouds stored in advance and local point clouds obtained by a vehicle in real time to be preset resolution ratios, wherein the preset resolution ratios are lower than the initial resolution ratios of the local point cloud information;
performing iterative matching until an iterative exit condition is satisfied:
randomly selecting N particles from a sampling range of the map point cloud as matching particles, carrying out ICP (inductively coupled plasma) matching on each matching particle and the particles of the local point cloud to obtain a matching estimation coefficient of each matching particle, and calculating according to the projection area of the map point cloud on the ground plane and the resolution of the current map point cloud to obtain N;
according to the matching estimation coefficient, carrying out particle filtering on the matched particles to obtain stable effective matched particles;
updating the map point cloud according to the effective matching particles and reducing the sampling range;
improving the resolution of the map point cloud and the local point cloud, if the iteration exit condition is met, exiting the iteration matching, and otherwise, executing the next iteration matching;
and if the iterative matching is exited, determining a point cloud positioning result according to the effective matching particles when the iterative matching is exited.
Furthermore, the step of uniformly adjusting the resolution of the map point cloud stored in advance and the local point cloud acquired by the vehicle in real time to a preset resolution includes:
converting a pre-stored map point cloud into an octree structure to obtain a map octree structure, and converting a local point cloud obtained by a vehicle in real time into the octree structure to obtain the local octree structure;
and adjusting the map octree structure and the local octree structure to the level corresponding to the preset resolution.
Still further, the obtaining of stable effective matching particles by particle filtering the matching particles according to the matching estimation coefficients includes:
calculating an observation probability function, a state transfer function and a point cloud density distribution function according to the matching estimation coefficient;
according to observation probability function, state transfer function and point cloud density distribution functionCarrying out particle filtering to obtain the effective particle number Neff；
For the ith matched particle in the kth iteration of particle filtering, the matching estimation coefficient is
Figure BDA0001180328340000061
Figure BDA0001180328340000062
Wherein the content of the first and second substances,
Figure BDA0001180328340000063
for the map point cloud used in the kth iteration of particle filtering, M is the number of matching particles included in the map point cloud used in the kth iteration of particle filtering,
Figure BDA0001180328340000064
for the rotation-translation matrix, p, of the ith matched particle in the kth iteration of the particle filteringpFor the p-th particle in the local point cloud, qqFor the q-th matching particle in the map point cloud, and ppAnd q isqCorresponding;
corresponding observation probability function is
Figure BDA0001180328340000065
The corresponding state transfer function is
Figure BDA0001180328340000066
Wherein
Figure BDA0001180328340000067
To match the position of particle i in the kth iteration of particle filtering,
Figure BDA0001180328340000068
for the rotation-translation matrix of the ith matched particle in the k-10 th iterative matching,
Figure BDA0001180328340000069
estimating coefficients for the matching of the ith matching particle in the k-10 iteration matching;
corresponding point cloud density distribution function of
Figure BDA0001180328340000071
Figure BDA0001180328340000072
Wherein, Node (x)k (i)) Denoted as the parent node of the ith matched particle in the kth iteration of particle filtering.
Still further, the iteration exit condition is:
the average efficiency of the effective matching particles is smaller than a preset efficiency threshold, wherein the average efficiency of the effective matching particles is calculated in the following way:
sorting according to the matching efficiency index of each matching particle, and sorting the top N of the resultseffTaking the matched particles as effective matched particles, and calculating to obtain the average efficiency of the effective matched particles, wherein the matching efficiency index corresponding to the ith matched particle in the kth iteration of the particle filter is
Figure BDA0001180328340000073
Figure BDA0001180328340000074
Wherein t isk (i)The computation time for the ith matched particle at the kth iteration of the particle filter.
Further, the updating the map point cloud according to the valid matching particles and narrowing the sampling range includes:
calculating the clustering center and the clustering radius of the effective matching particles;
taking the clustering center as a center, and taking a preset multiple of the clustering radius as a search range;
combining the particles in the search range in the map point cloud into a search map point cloud, and updating the map point cloud into the search map point cloud.
Further, determining a point cloud positioning result according to the valid matching particles when the iterative matching is exited comprises:
and taking the clustering center of the effective matching particles when the iterative matching is exited as a point cloud positioning result.
Still further, the apparatus further comprises:
a fusion positioning module to: and fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
Still further, the fusion positioning module is specifically configured to:
and fusing the point cloud positioning result and the navigation positioning result through extended Kalman filtering to obtain a fusion positioning result.
An embodiment of the present invention provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the one processor to cause the at least one processor to:
and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud.
Further, the processor is further capable of:
and fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
The method and the device for locating the local point cloud have the advantages that the local point cloud obtained by the vehicle in real time and the map point cloud stored in advance are used for locating, a point cloud locating result is obtained, and the resolution ratio of the local point cloud obtained by the vehicle in real time is lower than that of the map point cloud. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.
Drawings
Fig. 1 is a flowchart illustrating a vehicle positioning method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for locating a vehicle according to an alternative embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for locating a vehicle according to an alternative embodiment of the present invention;
FIG. 4 is a block diagram of an apparatus for positioning a vehicle according to an embodiment of the present invention;
FIG. 5 is a block diagram of an apparatus for a vehicle positioning device in accordance with an alternative embodiment of the present invention;
fig. 6 is a schematic hardware structure diagram of an electronic device for executing a vehicle positioning method according to an eighth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
Example one
Fig. 1 is a flowchart illustrating a vehicle positioning method according to an embodiment of the present invention, including:
and S101, positioning by using local point clouds acquired by a vehicle in real time and map point clouds stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point clouds is higher than that of the local point clouds.
The map point cloud is a high-precision map, and can be provided by offline high-precision matching (for example, by adopting a General-ICP method which is time-consuming but can obtain higher precision than the ICP method) after being acquired online by a high-resolution laser device.
And then, taking the high-precision map point cloud as cloud data for a plurality of low-precision laser automatic driving systems to use simultaneously, executing the step S101 by the vehicle with the automatic driving system, acquiring local point cloud through low-resolution laser equipment (Velodyne16), and then matching and positioning the local point cloud and the high-precision map point cloud.
The method and the device for locating the local point cloud have the advantages that the local point cloud obtained by the vehicle in real time and the map point cloud stored in advance are used for locating, a point cloud locating result is obtained, and the resolution ratio of the local point cloud obtained by the vehicle in real time is lower than that of the map point cloud. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.
Example two
Fig. 2 is a flowchart illustrating a vehicle positioning method according to an alternative embodiment of the present invention, which includes:
step S201, uniformly adjusting the resolutions of map point clouds stored in advance and local point clouds acquired by a vehicle in real time to preset resolutions, wherein the initial resolution of the map point clouds is higher than that of the local point clouds, and the preset resolution is lower than that of the local point cloud information;
specifically, a map point cloud stored in advance is converted into an octree structure to obtain a map octree structure, and a local point cloud obtained by a vehicle in real time is converted into the octree structure to obtain a local octree structure;
and adjusting the map octree structure and the local octree structure to the level corresponding to the preset resolution.
The octree is a hierarchical three-dimensional data storage structure, and the search efficiency of neighbor search can be improved by converting map point clouds and local point clouds into corresponding octree structures.
Performing the following iterative matching until an iterative exit condition is satisfied, wherein the iterative exit condition is that:
the average efficiency of the effective matching particles is smaller than a preset efficiency threshold, wherein the average efficiency of the effective matching particles is calculated in the following way:
sorting according to the matching efficiency index of each matching particle, and sorting the top N of the resultseffTaking the matched particles as effective matched particles, and calculating to obtain the average efficiency of the effective matched particles, wherein the matching efficiency index corresponding to the ith matched particle in the kth iteration of the particle filter is
Figure BDA0001180328340000101
Figure BDA0001180328340000102
Wherein t isk (i)The computation time for the ith matched particle at the kth iteration of the particle filter.
Figure BDA0001180328340000111
Coefficients are estimated for the match of the ith matched particle in the kth iteration of particle filtering. By calculating the average efficiency of the effective particles
Figure BDA0001180328340000112
The potential matching efficiency at the current map resolution can be approximated if less than a given threshold THeffThen the particle filter for the current resolution is considered to have stabilized.
Step S202, selecting N particles as matching particles randomly from a sampling range of the map point cloud, carrying out ICP (inductively coupled plasma) matching on each matching particle and the particles of the local point cloud to obtain a matching estimation coefficient of each matching particle, and calculating according to the projection area of the map point cloud on the ground plane and the resolution of the current map point cloud to obtain N.
Wherein the content of the first and second substances,
Figure BDA0001180328340000113
wherein c is0Project(s) is the projected area of the map point cloud on the ground plane, and D is the resolution.
And step S203, obtaining stable effective matching particles by particle filtering on the matching particles according to the matching estimation coefficient.
Specifically, Particle Filter (PF) approximates a probability density function in a state space by random samples propagating in the state space, and its core idea is to optimize the Particle distribution by extracting the Particle distribution from the posterior probability and then using Sequential Importance Sampling (SIR).
The SIR-based particle filtering method comprises the following steps of firstly sampling in a state space for N times according to a particle distribution function at the previous moment, then correcting the weight of particles according to an observation probability function and a state transfer function at the current moment, normalizing weight information, and then calculating the number of effective particles according to the weight information of the particles.
Corresponding to the embodiment, the method calculates an observation probability function, a state transfer function and a point cloud density distribution function according to the matching estimation coefficient, wherein the point cloud density distribution function is a particle distribution function;
then, carrying out particle filtering according to an observation probability function, a state transfer function and a point cloud density distribution function to obtain the effective particle number Neff；
For the ith matched particle in the kth iteration of particle filtering, the matching estimation coefficient is
Figure BDA0001180328340000121
Figure BDA0001180328340000122
Wherein the content of the first and second substances,
Figure BDA0001180328340000123
for the map point cloud used in the kth iteration of particle filtering, M is the number of matching particles included in the map point cloud used in the kth iteration of particle filtering,
Figure BDA0001180328340000124
for the rotation-translation matrix, p, of the ith matched particle in the kth iteration of the particle filteringpFor the p-th particle in the local point cloud, qqFor the q-th matching particle in the map point cloud, and ppAnd q isqCorresponding;
corresponding observation probability function is
Figure BDA0001180328340000125
The corresponding state transfer function is
Figure BDA0001180328340000126
Wherein
Figure BDA0001180328340000127
To match the position of particle i in the kth iteration of particle filtering,
Figure BDA0001180328340000128
for the rotational-translation matrix of the ith matched particle in the (k-1) th iterative matching,
Figure BDA0001180328340000129
estimating coefficients for the matching of the ith matching particle in the k-1 th iteration matching;
corresponding point cloud density distribution function of
Figure BDA00011803283400001210
Figure BDA00011803283400001211
Wherein, Node (x)k (i)) Denoted as the parent node of the ith matched particle in the kth iteration of particle filtering.
And step S204, updating the map point cloud according to the effective matching particles and reducing the sampling range.
Specifically, the cluster center and the cluster radius of the effectively matched particles are calculated;
taking the clustering center as a center, and taking a preset multiple of the clustering radius as a search range;
combining the particles in the search range in the map point cloud into a search map point cloud, and updating the map point cloud into the search map point cloud.
And S205, improving the resolution of the map point cloud and the local point cloud, exiting the step S206 of iterative matching if the iterative exit condition is met, and otherwise executing the step S202 of next iterative matching.
Executing step S202 will reselect N matching particles, i.e. perform a particle resampling process.
And step S206, if the iterative matching is exited, determining a point cloud positioning result according to the effective matching particles when the iterative matching is exited.
Specifically, the clustering center of the effective matching particle when the iterative matching is exited is used as the point cloud positioning result.
The embodiment of the invention realizes a specific method for positioning by using local point cloud acquired by a vehicle in real time and map point cloud stored in advance by fusing an ICP algorithm and a particle filter algorithm. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.
EXAMPLE III
Fig. 3 is a flowchart illustrating a vehicle positioning method according to another alternative embodiment of the present invention, including:
step S301, positioning is carried out by using local point clouds obtained by a vehicle in real time and map point clouds stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point clouds is higher than that of the local point clouds.
And S302, fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
Specifically, the point cloud positioning result and the navigation positioning result are fused through extended Kalman filtering to obtain a fusion positioning result.
Since the stable distribution frequency of the laser device is generally 10hz, the positioning distribution result based on multi-resolution in step S301 can only reach 10hz at most, and the environment sensing algorithm and the path planning algorithm of the autopilot system generally need higher positioning distribution frequency. To illustrate this problem, taking the vehicle speed as 80km/h (22m/s) as an example, if the issuance frequency is 10hz, the relative pose is updated every 2.2m advance of the vehicle body; this distance is reduced to 0.44m if issued at a frequency of 50 Hz.
In order to improve the release frequency of the positioning information, the embodiment adopts an extended Kalman filtering method to perform fusion positioning. And fusing the 10Hz positioning result issued by laser positioning and the 50Hz result issued by the combined navigation into a 50Hz positioning issuing result through EKF filtering.
According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced. Meanwhile, the point cloud positioning result and the navigation positioning result are fused to obtain a fusion positioning result, so that the issuing frequency of the positioning result is improved.
Example four
Fig. 4 is a block diagram of an apparatus of a vehicle positioning apparatus according to an embodiment of the present invention, including:
a point cloud positioning module 401 configured to: and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud.
The method and the device for locating the local point cloud have the advantages that the local point cloud obtained by the vehicle in real time and the map point cloud stored in advance are used for locating, a point cloud locating result is obtained, and the resolution ratio of the local point cloud obtained by the vehicle in real time is lower than that of the map point cloud. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.
EXAMPLE five
An alternative embodiment of the present invention provides a vehicle positioning apparatus, including:
a point cloud localization module to: uniformly adjusting the resolution ratios of a map point cloud stored in advance and a local point cloud obtained by a vehicle in real time to be preset resolution ratios, wherein the initial resolution ratio of the map point cloud is higher than that of the local point cloud, and the preset resolution ratio is lower than that of the local point cloud information;
specifically, a map point cloud stored in advance is converted into an octree structure to obtain a map octree structure, and a local point cloud obtained by a vehicle in real time is converted into the octree structure to obtain a local octree structure;
and adjusting the map octree structure and the local octree structure to the level corresponding to the preset resolution.
Performing the following iterative matching until an iterative exit condition is satisfied, wherein the iterative exit condition is that:
the average efficiency of the effective matching particles is smaller than a preset efficiency threshold, wherein the average efficiency of the effective matching particles is calculated in the following way:
sorting according to the matching efficiency index of each matching particle, and sorting the top N of the resultseffTaking the matched particles as effective matched particles, and calculating to obtain the average efficiency of the effective matched particles, wherein the matching efficiency index corresponding to the ith matched particle in the kth iteration of the particle filter is
Figure BDA0001180328340000151
Figure BDA0001180328340000152
Wherein t isk (i)The computation time for the ith matched particle at the kth iteration of the particle filter.
Figure BDA0001180328340000153
Coefficients are estimated for the match of the ith matched particle in the kth iteration of particle filtering. By calculating the average efficiency of the effective particles
Figure BDA0001180328340000154
The potential matching efficiency at the current map resolution can be approximated if less than a given threshold THeffThen the particle filter for the current resolution is considered to have stabilized.
And randomly selecting N particles from the sampling range of the map point cloud as matching particles, carrying out ICP (inductively coupled plasma) matching on each matching particle and the particles of the local point cloud to obtain a matching estimation coefficient of each matching particle, and calculating according to the projection area of the map point cloud on the ground plane and the resolution of the current map point cloud to obtain N.
Wherein the content of the first and second substances,
Figure BDA0001180328340000161
wherein c is0Project(s) is the projected area of the map point cloud on the ground plane, and D is the resolution.
And carrying out particle filtering on the matched particles according to the matching estimation coefficient to obtain stable effective matched particles.
Specifically, an observation probability function, a state transfer function, a point cloud density distribution function and a point cloud density distribution function are calculated according to the matching estimation coefficient;
carrying out particle filtering according to an observation probability function, a state transfer function and a point cloud density distribution function to obtain the effective particle number Neff；
For the ith matched particle in the kth iteration of particle filtering, the matching estimation coefficient is
Figure BDA0001180328340000162
Figure BDA0001180328340000163
Wherein the content of the first and second substances,
Figure BDA0001180328340000164
for the map point cloud used in the kth iteration of particle filtering, M is the number of matching particles included in the map point cloud used in the kth iteration of particle filtering,
Figure BDA0001180328340000165
for the rotation-translation matrix, p, of the ith matched particle in the kth iteration of the particle filteringpFor the p-th particle in the local point cloud, qqFor the q-th matching particle in the map point cloud, and ppAnd q isqCorresponding;
corresponding observation probability function is
Figure BDA0001180328340000166
The corresponding state transfer function is
Figure BDA0001180328340000167
Wherein
Figure BDA0001180328340000168
To match the position of particle i in the kth iteration of particle filtering,
Figure BDA0001180328340000171
for the rotational-translation matrix of the ith matched particle in the (k-1) th iterative matching,
Figure BDA0001180328340000172
estimating coefficients for the matching of the ith matching particle in the k-1 th iteration matching;
corresponding point cloud density distribution function of
Figure BDA0001180328340000173
Figure BDA0001180328340000174
Wherein, Node (x)k (i)) Denoted as the parent node of the ith matched particle in the kth iteration of particle filtering.
And updating the map point cloud according to the effective matching particles and reducing the sampling range.
Specifically, the cluster center and the cluster radius of the effectively matched particles are calculated;
taking the clustering center as a center, and taking a preset multiple of the clustering radius as a search range;
combining the particles in the search range in the map point cloud into a search map point cloud, and updating the map point cloud into the search map point cloud.
And (4) improving the resolution of the map point cloud and the local point cloud, exiting the iterative matching if an iterative exit condition is met, and otherwise executing the next iterative matching.
And if the iterative matching is exited, determining a point cloud positioning result according to the effective matching particles when the iterative matching is exited.
Specifically, the clustering center of the effective matching particle when the iterative matching is exited is used as the point cloud positioning result.
The embodiment of the invention realizes a specific method for positioning by using local point cloud acquired by a vehicle in real time and map point cloud stored in advance by fusing an ICP algorithm and a particle filter algorithm. According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced.
EXAMPLE six
Fig. 5 is a block diagram illustrating an apparatus of a vehicle positioning apparatus according to another alternative embodiment of the present invention, including:
a point cloud positioning module 501, configured to: and positioning the local point cloud acquired by the vehicle in real time and a map point cloud stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point cloud is higher than that of the local point cloud.
A fusion localization module 502 configured to: and fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
Specifically, the point cloud positioning result and the navigation positioning result are fused through extended Kalman filtering to obtain a fusion positioning result.
According to the embodiment of the invention, the local point cloud can be obtained through the low-resolution laser equipment and is matched and positioned with the high-precision map, so that the necessity of the high-precision laser equipment on the positioning problem is eliminated, and the positioning cost is reduced. Meanwhile, the point cloud positioning result and the navigation positioning result are fused to obtain a fusion positioning result, so that the issuing frequency of the positioning result is improved.
A seventh embodiment of the present invention provides a non-transitory computer storage medium having stored thereon computer-executable instructions operable to perform the vehicle localization method of any of the method embodiments described above.
Fig. 6 is a schematic diagram of a hardware structure of an electronic device for executing a vehicle positioning method according to an eighth embodiment of the present invention, which mainly includes: at least one processor 610; and a memory 620 communicatively coupled to the at least one processor 610; the memory 620 stores instructions executable by the at least one processor 610, so that the at least one processor can execute the method flows shown in fig. 1, fig. 2, and fig. 3, for example, one processor 610 is shown in fig. 6.
The electronic device performing the vehicle positioning method may further include: an input device 630 and an output device 640.
The processor 610, the memory 620, the input device 630, and the display device 640 may be connected by a bus or other means, and fig. 6 illustrates an example of a bus connection.
The memory 620, as a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the vehicle localization method in the embodiments of the present application, for example, the method flows shown in fig. 1, fig. 2, and fig. 3, or the point cloud localization module 401 shown in fig. 4, or the point cloud localization module 501 and the fusion localization module 502 shown in fig. 5. The processor 610 executes various functional applications and data processing by executing nonvolatile software programs, instructions, and modules stored in the memory 620, that is, implements the vehicle positioning method in the above-described embodiment.
The memory 620 may 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 stored data area may store data created according to use of the vehicle locating device, and the like. Further, the memory 620 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 620 optionally includes memory located remotely from the processor 610, and such remote memory may be connected over a network to a device that performs the vehicle localization method. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may receive input user clicks and generate signal inputs related to user settings and functional control of the vehicle positioning device. The display device 640 may include a display screen or the like.
The vehicle localization method of any of the above method embodiments is performed when the one or more modules are stored in the memory 620 and executed by the one or more processors 610.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.
The electronic device of embodiments of the present invention exists in a variety of forms, including but not limited to:
(1) an Electronic Control Unit (ECU) is also called a "traveling computer" or a "vehicle-mounted computer". The digital signal processor mainly comprises a microprocessor (CPU), a memory (ROM and RAM), an input/output interface (I/O), an analog-to-digital converter (A/D), a shaping circuit, a driving circuit and other large-scale integrated circuits.
(2) Mobile communication devices, which are characterized by mobile communication capabilities and are primarily targeted at providing voice and data communications. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(3) The ultra-mobile personal computer equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include PDA, MID, and UMPC devices, such as ipads.
(4) Portable entertainment devices such devices may display and play multimedia content. Such devices include audio and video players (e.g., ipods), handheld game consoles, electronic books, as well as smart toys and portable car navigation devices.
(5) The server is similar to a general computer architecture, but has higher requirements on processing capability, stability, reliability, safety, expandability, manageability and the like because of the need of providing highly reliable services.
(6) And other electronic devices with data interaction functions.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a mobile terminal (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and not to limit the same; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A vehicle positioning method, characterized by comprising:
positioning local point clouds obtained by a vehicle in real time and map point clouds stored in advance to obtain a point cloud positioning result, wherein the initial resolution of the map point clouds is higher than that of the local point clouds;
the method comprises the following steps of positioning local point cloud obtained by using a vehicle in real time and map point cloud stored in advance to obtain a point cloud positioning result, wherein the point cloud positioning result comprises the following steps:
uniformly adjusting the resolution ratios of map point clouds stored in advance and local point clouds obtained by a vehicle in real time to be preset resolution ratios, wherein the preset resolution ratios are lower than the initial resolution ratios of the local point cloud information;
performing iterative matching until an iterative exit condition is satisfied:
randomly selecting N particles from a sampling range of the map point cloud as matching particles, carrying out ICP (inductively coupled plasma) matching on each matching particle and the particles of the local point cloud to obtain a matching estimation coefficient of each matching particle, and calculating according to the projection area of the map point cloud on the ground plane and the resolution of the current map point cloud to obtain N;
according to the matching estimation coefficient, carrying out particle filtering on the matched particles to obtain stable effective matched particles;
updating the map point cloud according to the effective matching particles and reducing the sampling range;
improving the resolution of the map point cloud and the local point cloud, if the iteration exit condition is met, exiting the iteration matching, and otherwise, executing the next iteration matching;
and if the iterative matching is exited, determining a point cloud positioning result according to the effective matching particles when the iterative matching is exited.
2. The vehicle positioning method according to claim 1, wherein the step of uniformly adjusting the resolution of the map point cloud saved in advance and the local point cloud obtained by the vehicle in real time to a preset resolution comprises the steps of:
converting a pre-stored map point cloud into an octree structure to obtain a map octree structure, and converting a local point cloud obtained by a vehicle in real time into the octree structure to obtain the local octree structure;
and adjusting the map octree structure and the local octree structure to the level corresponding to the preset resolution.
3. The vehicle positioning method according to claim 2, wherein the obtaining of stable effective matching particles by particle filtering the matching particles according to the matching estimation coefficients comprises:
calculating an observation probability function, a state transfer function and a point cloud density distribution function according to the matching estimation coefficient;
carrying out particle filtering according to an observation probability function, a state transfer function and a point cloud density distribution function to obtain the effective particle number Neff；
For in the k-th iteration of particle filteringThe ith matched particle with a matching estimation coefficient of
Figure FDA0002948846600000021
Figure FDA0002948846600000022
Wherein the content of the first and second substances,
Figure FDA0002948846600000023
for the map point cloud used in the kth iteration of particle filtering, M is the number of matching particles included in the map point cloud used in the kth iteration of particle filtering,
Figure FDA0002948846600000024
for the rotation-translation matrix, p, of the ith matched particle in the kth iteration of the particle filteringpFor the p-th particle in the local point cloud, qqFor the q-th matching particle in the map point cloud, and ppAnd q isqCorresponding;
corresponding observation probability function is
Figure FDA0002948846600000025
The corresponding state transfer function is
Figure FDA0002948846600000026
Wherein
Figure FDA0002948846600000027
To match the position of particle i in the kth iteration of particle filtering,
Figure FDA0002948846600000028
for the rotational-translation matrix of the ith matched particle in the (k-1) th iterative matching,
Figure FDA0002948846600000029
estimating coefficients for the matching of the ith matching particle in the k-1 th iteration matching;
corresponding point cloud density distribution function of
Figure FDA0002948846600000031
Wherein, Node (x)k (i)) Denoted as the parent node of the ith matched particle in the kth iteration of particle filtering.
4. The vehicle localization method according to claim 3, wherein the iteration exit condition is:
the average efficiency of the effective matching particles is smaller than a preset efficiency threshold, wherein the average efficiency of the effective matching particles is calculated in the following way:
sorting according to the matching efficiency index of each matching particle, and sorting the top N of the resultseffTaking the matched particles as effective matched particles, and calculating to obtain the average efficiency of the effective matched particles, wherein the matching efficiency index corresponding to the ith matched particle in the kth iteration of the particle filter is
Figure FDA0002948846600000032
Figure FDA0002948846600000033
Wherein t isk (i)The computation time for the ith matched particle at the kth iteration of the particle filter.
5. The vehicle localization method of claim 1, wherein the updating and narrowing the sampling range of the map point cloud according to the valid matching particles comprises:
calculating the clustering center and the clustering radius of the effective matching particles;
taking the clustering center as a center, and taking a preset multiple of the clustering radius as a search range;
combining the particles in the search range in the map point cloud into a search map point cloud, and updating the map point cloud into the search map point cloud.
6. The vehicle localization method according to claim 1, wherein determining a point cloud localization result according to valid matching particles when exiting iterative matching comprises:
and taking the clustering center of the effective matching particles when the iterative matching is exited as a point cloud positioning result.
7. The vehicle positioning method according to any one of claims 1 to 6, characterized by further comprising:
and fusing the point cloud positioning result and the navigation positioning result to obtain a fusion positioning result.
8. The vehicle positioning method according to claim 7, wherein the fusing the point cloud positioning result and the navigation positioning result to obtain a fused positioning result comprises:
and fusing the point cloud positioning result and the navigation positioning result through extended Kalman filtering to obtain a fusion positioning result.
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