CN112154355A - High-precision map positioning method, system, platform and computer readable storage medium - Google Patents

High-precision map positioning method, system, platform and computer readable storage medium Download PDF

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Publication number
CN112154355A
CN112154355A CN201980032943.3A CN201980032943A CN112154355A CN 112154355 A CN112154355 A CN 112154355A CN 201980032943 A CN201980032943 A CN 201980032943A CN 112154355 A CN112154355 A CN 112154355A
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positioning result
candidate
height
determining
matching degree
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CN112154355B (en
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钟阳
周游
孙路
江灿森
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Shenzhen Zhuoyu Technology Co ltd
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SZ DJI Technology Co Ltd
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S17/00Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88Lidar systems specially adapted for specific applications
    • G01S17/89Lidar systems specially adapted for specific applications for mapping or imaging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30Map- or contour-matching
    • G01C21/32Structuring or formatting of map data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S17/00Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88Lidar systems specially adapted for specific applications
    • G01S17/93Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Automation & Control Theory (AREA)
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Abstract

A high-precision map positioning method, a system, a platform and a computer readable storage medium are provided, the method comprises: obtaining an off-line high-precision map, and establishing an on-line point cloud map; performing rasterization processing on the online point cloud map to obtain a plurality of online raster maps; and positioning the movable platform according to the height intervals in the plurality of online grid maps and the height intervals in the offline high-precision map. The method improves the positioning accuracy.

Description

High-precision map positioning method, system, platform and computer readable storage medium
Technical Field
The present application relates to the field of high-precision maps, and in particular, to a high-precision map positioning method, system, platform, and computer-readable storage medium.
Background
With the development of map technology, high-precision maps are beginning to be used in more and more fields. In general, high-precision map positioning is to obtain the surrounding environment through a sensor mounted on a movable platform and obtain some characteristic information of the surrounding environment, and then match the characteristic information with the characteristic information in a high-precision map, so as to obtain the positioning of the movable platform in the high-precision map.
However, since the feature matching is relatively dependent on the information richness of the surrounding environment, the situation that the positioning cannot be performed may occur in some scenes with sparse features or lack of obvious features; and for the case of repetitive features in some environments, erroneous matching results may occur. Therefore, how to improve the accuracy and stability of the high-precision map positioning result is a problem to be solved urgently at present.
Disclosure of Invention
Based on the above, the application provides a high-precision map positioning method, system, platform and computer readable storage medium, aiming at improving the accuracy and stability of the high-precision map positioning result.
In a first aspect, the present application provides a high-precision map positioning method, including:
acquiring an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result to obtain a first positioning result of the movable platform.
In a second aspect, the present application further provides a high-precision map positioning method, including:
acquiring an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
In a third aspect, the present application further provides a driving system comprising a lidar, a memory, and a processor; the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, to obtain a first positioning result of the movable platform.
In a fourth aspect, the present application further provides a driving system comprising a lidar, a memory, and a processor; the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
In a fifth aspect, the present application further provides a movable platform comprising a lidar, a memory, and a processor; the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, to obtain a first positioning result of the movable platform.
In a sixth aspect, the present application further provides a movable platform comprising a lidar, a memory, and a processor; the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
In a seventh aspect, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to implement the high-precision map positioning method as described above.
The embodiment of the application provides a high-precision map positioning method, a high-precision map positioning system, a high-precision map positioning platform and a computer-readable storage medium.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a high-precision map positioning method according to an embodiment of the present application;
FIG. 2 is a flow diagram illustrating sub-steps of the high accuracy map location method of FIG. 1;
FIG. 3 is a flow chart illustrating steps of another high-precision mapping method according to an embodiment of the present application;
FIG. 4 is a flow diagram illustrating sub-steps of the high accuracy map location method of FIG. 3;
FIG. 5 is a flow chart illustrating steps of another high-precision map location method according to an embodiment of the present application;
FIG. 6 is a flow chart illustrating steps of another high-precision mapping method according to an embodiment of the present application;
FIG. 7 is a block diagram schematically illustrating a driving system according to an embodiment of the present disclosure;
fig. 8 is a block diagram illustrating a structure of a movable platform according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating steps of a high-precision map positioning method according to an embodiment of the present disclosure. The high-precision map positioning method can be applied to a movable platform or a driving system. The movable platform comprises a vehicle and an aircraft, the aircraft comprises an unmanned aircraft and a manned aircraft, the vehicle comprises a manned vehicle, an unmanned vehicle and the like, and the unmanned aircraft comprises a rotary wing type unmanned aircraft, such as a four-rotor unmanned aircraft, a six-rotor unmanned aircraft and an eight-rotor unmanned aircraft, or a fixed wing type unmanned aircraft, or a combination of a rotary wing type and a fixed wing type unmanned aircraft, which is not limited herein.
Specifically, as shown in fig. 1, the high-precision map positioning method includes steps S101 to S103.
S101, obtaining an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises a plurality of first height intervals.
The movable platform collects three-dimensional point cloud data of a driving area through a high-precision laser radar, and processes the collected three-dimensional point cloud data through a high-precision inertial navigation system and a point cloud registration algorithm to generate an off-line high-precision map. Or acquiring three-dimensional point cloud data of a running area by using a high-precision laser radar, acquiring attitude data of the movable platform by using an Inertial Measurement Unit (IMU), acquiring position data of the movable platform by using a Global Positioning System (GPS), correcting the acquired three-dimensional point cloud data based on the attitude data and the position data, and generating an offline high-precision map based on the corrected three-dimensional point cloud data.
The method comprises the steps that an off-line high-precision map is obtained by a movable platform in the moving process, three-dimensional point cloud data of objects around the movable platform are collected in real time through a laser radar, and an on-line point cloud map is established based on the three-dimensional point cloud data collected in real time. The laser radar can determine the three-dimensional point cloud data of the object based on the distance between the laser emission point and the reflection point of the emitted laser on the object and the emission direction of the laser emission point. The three-dimensional point cloud data of the objects around the movable platform comprises data such as the distance between the objects and the movable platform, the angle between the objects and the movable platform, and the three-dimensional coordinates of the objects.
The off-line high-precision map comprises a plurality of first height intervals, the height of each grid in the off-line high-precision map is respectively located in the corresponding first height intervals, the first height intervals are obtained by dividing according to the height of the three-dimensional point cloud, the height interval values of the first height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height intervals. It should be noted that the height interval value and the number of height intervals may be set based on actual conditions, and the present application is not limited to this. For example, if the height is 26 meters, the first height intervals obtained by dividing are 7 first height intervals in total, namely [0, 1 ], [1, 5 ], [5, 9 ], [9, 14 ], [14, 18 ], [18, 22 ], and [22, 26], respectively.
S102, determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals.
The mobile platform determines a candidate positioning result set, and performs rasterization processing on the online point cloud map according to each candidate positioning result to obtain an online raster map corresponding to each candidate positioning result, that is, each candidate positioning result in the candidate positioning result set is marked in the online point cloud map, and performs rasterization processing on a map area around each marked candidate positioning result to obtain a raster map corresponding to each candidate positioning result.
The online grid map comprises a plurality of second height intervals, the height of each grid in the online grid map is respectively located in the corresponding second height intervals, the second height intervals are obtained by dividing according to the height of the three-dimensional point cloud, the height interval values of the second height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height intervals. It should be noted that the height interval value and the number of height intervals may be set based on actual conditions, and the present application is not limited to this. For example, if the height is 26 meters, the divided second height intervals are 7 second height intervals in total, namely [0, 1 ], [1, 5 ], [5, 9 ], [9, 14 ], [14, 18 ], [18, 22 ], and [22, 26], respectively.
It should be noted that, the number of the first height sections may be the same as or different from that of the second height sections, the first height sections and the second height sections have a corresponding relationship, and two end points of the first height sections and the second height sections having the corresponding relationship are the same, which is not specifically limited in this application.
In an embodiment, the determination method of the candidate positioning result set specifically includes: acquiring current position data and current attitude data of the movable platform; determining a candidate position set according to the current position data; determining a candidate attitude set according to the current attitude data and a preset attitude error value; and determining a candidate positioning result set according to the candidate position set and the candidate attitude set. The current position data of the movable platform is position data output by a positioning system of the movable platform at the current moment, and the current attitude data of the movable platform is attitude data output by an inertial measurement unit of the movable platform at the current moment. The position data includes geographic position coordinates of the movable platform and the attitude data includes pitch angle, roll angle, and yaw angle of the movable platform.
In an embodiment, the method for determining the candidate location set according to the current location data specifically includes: the movable platform determines the variation trend of the current position data, and determines a candidate position set according to the variation trend of the current position data. Specifically, the change trend may be represented by gradient values and gradient directions of the current position data, that is, a preset unit gradient is obtained, based on the gradient values, a preset number of candidate gradient values are obtained along the gradient direction, and position information corresponding to each candidate gradient value is determined, so as to determine a candidate position set. It should be noted that the preset unit gradient and the preset number may be set based on actual situations, and this is not specifically limited in this application.
In one embodiment, the set of candidate poses can be determined by: calculating a difference value between an attitude angle in current attitude data and a preset attitude error value, calculating a sum of the attitude angle in the current attitude data and the preset attitude error value, determining a candidate attitude set based on the difference value between the attitude angle in the current attitude data and the preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value, namely taking the difference value between the attitude angle and the preset attitude error as an end point and the sum of the attitude angle and the preset attitude error value as another end point to obtain a candidate attitude angle range, and acquiring a plurality of candidate attitude angles from the candidate attitude angle range by using a preset unit attitude angle, thereby forming the candidate attitude set. It should be noted that the preset attitude error value and the unit attitude angle may be set based on actual conditions, and this is not specifically limited in this application. The candidate attitude set can be quickly and accurately determined through the current attitude data and the preset attitude error value.
In an embodiment, the candidate pose set may be determined by: the method comprises the steps that a movable platform determines position coordinates of the movable platform in an offline high-precision map according to current position data, namely the geographical position coordinates of the movable platform are obtained from the current position data, the geographical position coordinates are marked in the offline high-precision map, then position coordinates of objects around the geographical position coordinates in the offline high-precision map are obtained, and the position coordinates of the movable platform in the offline high-precision map are determined based on the position coordinates of the surrounding objects in the offline high-precision map; and determining a candidate position set according to the position coordinates and a preset position error value. The candidate position set can be quickly and accurately determined through the current position data and the preset position error value.
In an embodiment, according to the candidate position set and the candidate pose set, the method for determining the candidate positioning result set specifically includes: and the movable platform selects a candidate position from the candidate position set and combines with each candidate gesture in the candidate gesture set each time until the candidate positions in the candidate position set are all selected once, and each candidate positioning result obtained by combination is collected to be used as a candidate positioning result set.
In an embodiment, the determination manner of the candidate positioning result set may further be: and acquiring a historical positioning result of the movable platform, and determining a candidate positioning result set according to the historical positioning result. The historical positioning result is a positioning result determined by the movable platform at the previous moment, and the previous moment and the current moment are separated by preset time. It should be noted that the preset time may be set based on actual situations, and the present application is not limited to this.
Specifically, historical position coordinates and historical attitude angles are obtained from the historical positioning result; performing derivation processing on the historical position coordinates to determine gradient values and gradient directions of the historical position coordinates, determining a candidate position set according to the gradient values and the gradient directions, namely obtaining a preset number of candidate gradient values along the gradient direction according to a preset unit gradient and determining position information corresponding to each candidate gradient value, thereby determining the candidate position set;
calculating a difference value between the historical attitude angle and a preset attitude error value, and calculating the sum of the historical attitude angle and the preset attitude error value; taking the difference value of the historical attitude angle and the preset attitude error value as an end point, taking the sum of the historical attitude angle and the preset attitude error value as another end point to obtain a candidate attitude angle range, and acquiring a plurality of candidate attitude angles from the candidate attitude angle range by using a preset unit attitude angle to form a candidate attitude set;
and determining a candidate positioning result set according to the candidate position set and the candidate posture set, namely, selecting a candidate position from the candidate position set and combining with each candidate posture in the candidate posture set each time, and collecting each candidate positioning result obtained by combination as the candidate positioning result set when the candidate positions in the candidate position set are all selected once. It should be noted that the preset unit gradient, the preset number, the preset attitude error value, and the preset unit attitude angle may be set based on an actual situation, and this is not specifically limited in this application.
S103, positioning the movable platform according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result to obtain the first positioning result of the movable platform.
After the movable platform determines the online grid map corresponding to each candidate positioning result, the movable platform is positioned according to a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in the online grid map corresponding to each candidate positioning result, and a first positioning result of the movable platform is obtained. The movable platform is positioned through a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in each online grid map, so that an accurate positioning result can be obtained, and the accuracy and the stability of the positioning result of the high-precision map can be improved.
In an embodiment, as shown in fig. 2, step S103 specifically includes: and substeps 1031 to S1032.
And S1031, determining the matching degree corresponding to each candidate positioning result according to the plurality of first height intervals in the off-line high-precision map and the plurality of second height intervals in the on-line grid map corresponding to each candidate positioning result.
Specifically, determining a state comparison result between each second height interval in each online grid map and a corresponding first height interval in the offline high-precision map; and counting the number of second height intervals with the state comparison result as the preset state comparison result in each online grid map, and taking the number of second height intervals with the state comparison result as the preset state comparison result in each online grid map as the matching degree corresponding to each candidate positioning result. Wherein the states of the first and second height sections include an occupied state and a non-occupied state, and the three-dimensional point cloud is present in the first or second height section in the occupied state, and the three-dimensional point cloud is absent in the first or second height section in the non-occupied state. The state comparison result comprises different states, both occupied states and both unoccupied states. Optionally, the preset state comparison result is "all occupied states".
In an embodiment, the determining manner of the matching degree corresponding to each candidate positioning result is specifically as follows: determining each second height interval in each online non-ground height layer and a state comparison result between the corresponding first height interval in each offline non-ground height layer; counting the number of second height intervals with the state comparison result as a preset state comparison result in each online non-ground height map layer; and determining the matching degree corresponding to each candidate positioning result according to the number of the second height intervals with the state comparison result as the preset state comparison result in each online non-ground height layer, namely, taking the number of the second height intervals with the state comparison result as the preset state comparison result in each online non-ground height layer as the matching degree corresponding to each candidate positioning result.
The off-line high-precision map comprises an off-line non-ground height layer, a plurality of first height intervals are located on the off-line non-ground height layer, the on-line grid map comprises an on-line non-ground height layer, and a plurality of second height intervals are located on the on-line non-ground height layer. The height of the end points of the first height interval located in the offline non-ground height layer and the second height interval located in the online non-ground height layer is greater than or equal to a set height threshold, and the height threshold may be set based on an actual situation, which is not specifically limited in this application. Optionally, if the height threshold is 1 meter, the height of 26 meters is divided into 6 second height intervals, where the first height interval is [1, 5 ], [5, 9 ], [9, 14 ], [14, 18 ], [18, 22 ] and [22, 26], and the second height interval is 6 second height intervals.
In an embodiment, the determination manner of the state comparison result between the first height interval and the corresponding second height interval is specifically as follows: acquiring first state identification information of each first height interval in each offline non-ground height layer and acquiring second state identification information of each second height interval in each online non-ground height layer; and determining each second height interval in each online non-ground height layer according to the first state identification information and each second state identification information, and comparing the state between the corresponding first height interval in the offline non-ground height layer. The first state identification information includes a state identifier corresponding to each first height interval, the second state identification information includes a state identifier corresponding to each second height interval, and the state identifier and the state represented by the state identifier may be set based on an actual situation, which is not specifically limited in this application. Alternatively, the altitude interval with the state identifier 0 is in the unoccupied state, and the altitude interval with the state identifier 1 is in the occupied state.
Specifically, the state identifier corresponding to each first height interval in the first state identification information and the state identifier corresponding to the second height interval in the second state identification information are subjected to logic and processing, so that a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer can be obtained. For example, if the number of the first height sections and the number of the second height sections are 7, the first state identification information is 1010101, and the second state identification information is 0111101, the bits are logically and-processed for 1010101 and 0111101, and the result is 0010100, the state comparison results between the 7 second height sections and the corresponding first height sections are respectively state difference, both occupied states, and both occupied states.
S1032, according to the matching degree corresponding to each candidate positioning result, selecting one candidate positioning result from the candidate positioning result set as the first positioning result of the movable platform.
After determining the respective matching degree of each candidate positioning result, the movable platform determines the first positioning result of the movable platform from the candidate positioning result set according to the respective matching degree of each candidate positioning result, that is, the candidate positioning result with the highest matching degree is taken as the first positioning result of the movable platform.
In the high-precision map positioning method provided by the embodiment, the on-line point cloud map is rasterized through each candidate positioning result in the candidate positioning result set to obtain the grid map corresponding to each candidate positioning result, and the movable platform is positioned through the plurality of height intervals in the off-line high-precision map and the plurality of height intervals in each on-line grid map, so that the movable platform can be positioned in scenes with sparse features or lack of obvious features, and the accuracy and the stability of the positioning result of the high-precision map can be improved.
Referring to fig. 3, fig. 3 is a schematic flowchart illustrating steps of another high-precision map positioning method according to an embodiment of the present application.
Specifically, as shown in fig. 3, the high-precision map positioning method includes steps S201 to S205.
S201, obtaining an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises a plurality of first height intervals.
The method comprises the steps that an off-line high-precision map is obtained by a movable platform in the moving process, three-dimensional point cloud data of objects around the movable platform are collected in real time through a laser radar, and an on-line point cloud map is established based on the three-dimensional point cloud data collected in real time. The laser radar can determine the three-dimensional point cloud data of the object based on the distance between the laser emission point and the reflection point of the emitted laser on the object and the emission direction of the laser emission point. The three-dimensional point cloud data of the objects around the movable platform comprises data such as the distance between the objects and the movable platform, the angle between the objects and the movable platform, and the three-dimensional coordinates of the objects.
S202, determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals.
The mobile platform determines a candidate positioning result set, and performs rasterization processing on the online point cloud map according to each candidate positioning result to obtain an online raster map corresponding to each candidate positioning result, that is, each candidate positioning result in the candidate positioning result set is marked in the online point cloud map, and performs rasterization processing on a map area around each marked candidate positioning result to obtain a raster map corresponding to each candidate positioning result.
S203, positioning the movable platform according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result to obtain the first positioning result of the movable platform.
After the movable platform determines the online grid map corresponding to each candidate positioning result, the movable platform is positioned according to a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in the online grid map corresponding to each candidate positioning result, and a first positioning result of the movable platform is obtained. The movable platform is positioned through a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in each online grid map, so that an accurate positioning result can be obtained, and the accuracy and the stability of the positioning result of the high-precision map can be improved.
S204, positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform.
And the movable platform positions the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform. The online grid map further comprises an online complete height map layer, wherein the height of each grid in the online complete height map layer is the mean height of three-dimensional point clouds in the grids; the off-line high-precision map further comprises an off-line complete height map layer, wherein the height of each grid in the off-line complete height map layer is the mean height of the three-dimensional point cloud in the grid.
In an embodiment, as shown in fig. 4, step S204 specifically includes: substeps 2041 to S2042.
S2041, determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result.
Specifically, the movable platform acquires a local offline complete height layer corresponding to each online complete height layer from the offline complete height layers; and determining the loss cost between each online complete height layer and the corresponding local offline complete height layer according to the height of each grid in each online complete height layer and the height of each grid in the corresponding local offline complete height layer, and taking the loss cost between each online complete height layer and the corresponding local offline complete height layer as the matching degree corresponding to each candidate positioning result.
S2042, according to the matching degree corresponding to each candidate positioning result, selecting one candidate positioning result from the candidate positioning result set as a second positioning result of the movable platform.
After determining the respective matching degree of each candidate positioning result, selecting one candidate positioning result from the candidate positioning result set as the second positioning result of the movable platform according to the respective matching degree of each candidate positioning result.
The determination mode of the second positioning result is specifically as follows: the movable platform checks the candidate positioning results in the candidate positioning result set according to the matching degree corresponding to each candidate positioning result; and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as the second positioning result of the movable platform. The verification mode of the candidate positioning result is specifically as follows: and determining whether the matching degree of the candidate positioning result is less than or equal to a preset matching degree threshold, if the matching degree of the candidate positioning result is less than or equal to the preset matching degree threshold, determining that the candidate positioning result passes the verification, and if the matching degree of the candidate positioning result is greater than the preset matching degree threshold, determining that the candidate positioning result does not pass the verification. It should be noted that the matching degree threshold may be set based on actual situations, and this is not specifically limited in this application. And taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result of the movable platform, so that the accuracy of the positioning result can be further improved.
S205, fusing the first positioning result and the second positioning result to obtain a target positioning result of the movable platform.
And after the first positioning result and the second positioning result of the movable platform are obtained, the movable platform fuses the first positioning result and the second positioning result to obtain a target positioning result of the movable platform. The accuracy and the stability of the positioning result can be further improved by fusing the two positioning results.
In one embodiment, the movable platform obtains the matching degree of the first positioning result and obtains the matching degree of the second positioning result; determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result; and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
The determination method of the first weight coefficient and the second weight coefficient specifically includes: normalizing the matching degree of the first positioning result and the matching degree of the second positioning result; determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result; determining a first weight coefficient of the first positioning result according to the matching degree and the total matching degree of the processed first positioning result; and determining a second weight coefficient of the second positioning result according to the matching degree and the total matching degree of the processed second positioning result.
The determination method of the weight coefficient specifically comprises the following steps: calculating the percentage of the matching degree of the processed first positioning result in the total matching degree, and taking the percentage as a first weight coefficient of the first positioning result; and calculating the percentage of the matching degree of the processed second positioning result in the total matching degree, and taking the percentage as a second weight coefficient of the second positioning result.
The determination mode of the target positioning result is specifically as follows: calculating the product of the first positioning result and the first weight coefficient to obtain a first weight positioning result; calculating the product of the second positioning result and the second weight coefficient to obtain a second weight positioning result; and calculating the sum of the first weight positioning result and the second weight positioning result, and taking the sum of the first weight positioning result and the second weight positioning result as the target positioning result of the movable platform.
In the high-precision map positioning method provided by the above embodiment, the on-line point cloud map is rasterized through each candidate positioning result in the candidate positioning result set to obtain the grid map corresponding to each candidate positioning result, the movable platform is positioned through a plurality of height intervals in the off-line high-precision map and a plurality of height intervals in each on-line grid map, and the movable platform is positioned through the on-line complete height layer and the off-line complete height layer corresponding to each candidate positioning result, and finally the two positioning results are fused, so that the accuracy and stability of the positioning result can be further improved.
Referring to fig. 5, fig. 5 is a schematic flowchart illustrating a procedure of another high-precision map positioning method according to an embodiment of the present application.
Specifically, as shown in fig. 5, the high-precision map positioning method includes steps S301 to S305.
S301, obtaining an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises an offline complete height map layer and an offline non-ground height map layer.
The movable platform collects three-dimensional point cloud data of a driving area through a high-precision laser radar, and processes the collected three-dimensional point cloud data through a high-precision inertial navigation system and a point cloud registration algorithm to generate an off-line high-precision map.
The method comprises the steps that an off-line high-precision map is obtained by a movable platform in the moving process, three-dimensional point cloud data of objects around the movable platform are collected in real time through a laser radar, and an on-line point cloud map is established based on the three-dimensional point cloud data collected in real time. The laser radar can determine the three-dimensional point cloud data of the object based on the distance between the laser emission point and the reflection point of the emitted laser on the object and the emission direction of the laser emission point. The three-dimensional point cloud data of the objects around the movable platform comprises data such as the distance between the objects and the movable platform, the angle between the objects and the movable platform, and the three-dimensional coordinates of the objects.
The off-line high-precision map comprises an off-line complete height map layer and an off-line non-ground height map layer, wherein the height of each grid in the off-line complete height map layer is the mean height of three-dimensional point cloud in the grid, the off-line non-ground height map layer comprises a plurality of height intervals, the height intervals are obtained by dividing the three-dimensional point cloud according to the height of the three-dimensional point cloud, the height interval values of the height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height interval. It should be noted that the height interval value and the number of height intervals may be set based on actual conditions, and the present application is not limited to this. For example, if the height is 26 meters, the height intervals obtained by dividing in the offline non-ground height map layer are respectively [1, 5 ], [5, 9 ], [9, 14 ], [14, 18 ], [18, 22 ], and [22, 26] for a total of 6 first height intervals.
S302, determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height layer and an online non-ground height layer.
The mobile platform determines a candidate positioning result set, and performs rasterization processing on the online point cloud map according to each candidate positioning result to obtain an online raster map corresponding to each candidate positioning result, that is, each candidate positioning result in the candidate positioning result set is marked in the online point cloud map, and performs rasterization processing on a map area around each marked candidate positioning result to obtain a raster map corresponding to each candidate positioning result.
The online grid map comprises an online complete height map layer and an online non-ground height map layer, wherein the height of each grid in the online complete height map layer is the mean height of three-dimensional point cloud in the grid, the online non-ground height map layer comprises a plurality of height intervals, the height intervals are obtained by dividing the three-dimensional point cloud according to the height of the three-dimensional point cloud, the height interval values of the height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height interval. It should be noted that the height interval value and the number of height intervals may be set based on actual conditions, and the present application is not limited to this.
In an embodiment, the determination method of the candidate positioning result set specifically includes: acquiring current position data and current attitude data of the movable platform; determining a candidate position set according to the current position data; determining a candidate attitude set according to the current attitude data and a preset attitude error value; and determining a candidate positioning result set according to the candidate position set and the candidate attitude set. The current position data of the movable platform is position data output by a positioning system of the movable platform at the current moment, and the current attitude data of the movable platform is attitude data output by an inertial measurement unit of the movable platform at the current moment. The position data includes geographic position coordinates of the movable platform and the attitude data includes pitch angle, roll angle, and yaw angle of the movable platform.
In an embodiment, the determination method of the candidate location set specifically includes: the movable platform determines the variation trend of the current position data, and determines a candidate position set according to the variation trend of the current position data. Specifically, the change trend may be represented by gradient values and gradient directions of the current position data, that is, a preset unit gradient is obtained, based on the gradient values, a preset number of candidate gradient values are obtained along the gradient direction, and position information corresponding to each candidate gradient value is determined, so as to determine a candidate position set. It should be noted that the preset unit gradient and the preset number may be set based on actual situations, and this is not specifically limited in this application.
In one embodiment, the set of candidate poses can be determined by: calculating a difference value between an attitude angle in current attitude data and a preset attitude error value, calculating a sum of the attitude angle in the current attitude data and the preset attitude error value, determining a candidate attitude set based on the difference value between the attitude angle in the current attitude data and the preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value, namely taking the difference value between the attitude angle and the preset attitude error as an end point and the sum of the attitude angle and the preset attitude error value as another end point to obtain a candidate attitude angle range, and acquiring a plurality of candidate attitude angles from the candidate attitude angle range by using a preset unit attitude angle, thereby forming the candidate attitude set. It should be noted that the preset attitude error value and the unit attitude angle may be set based on actual conditions, and this is not specifically limited in this application. The candidate attitude set can be quickly and accurately determined through the current attitude data and the preset attitude error value.
S303, positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result.
And the movable platform positions the movable platform according to the online non-ground height layer and the offline non-ground height layer corresponding to each candidate positioning result to obtain a first positioning result. Specifically, determining the matching degree between the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result; and according to the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result, determining a first positioning result from the candidate positioning result set, namely taking the candidate positioning result with the highest matching degree as the first positioning result of the movable platform. The point cloud matching is carried out on the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result, and according to the matching result, the positioning result of the movable platform can be obtained, and the accuracy and the stability of the positioning result are improved.
And recording the height interval in the offline non-ground height map layer as a first height interval, and recording the height interval in the offline non-ground height map layer as a second height interval, wherein the first height interval is the same as the corresponding second height interval.
In one embodiment, the movable platform determines a state comparison result between each second height interval in each online non-ground height layer and a corresponding first height interval in each offline non-ground height layer; counting the number of second height intervals with the state comparison result as a preset state comparison result in each online non-ground height map layer; and taking the number of second height intervals with the state comparison result in each online non-ground height layer as a preset state comparison result as the matching degree between the online non-ground height layer and the offline non-ground height layer corresponding to each candidate positioning result. Wherein the states of the first and second height sections include an occupied state and a non-occupied state, and the three-dimensional point cloud is present in the first or second height section in the occupied state, and the three-dimensional point cloud is absent in the first or second height section in the non-occupied state. The state comparison result comprises different states, both occupied states and both unoccupied states. Optionally, the preset state comparison result is "all occupied states".
In an embodiment, the determination manner of the state comparison result between the first height interval and the corresponding second height interval is specifically as follows: acquiring first state identification information of each first height interval in each offline non-ground height layer and acquiring second state identification information of each second height interval in each online non-ground height layer; and determining each second height interval in each online non-ground height layer according to the first state identification information and each second state identification information, and comparing the state between the corresponding first height interval in the offline non-ground height layer. The first state identification information includes a state identifier corresponding to each first height interval, the second state identification information includes a state identifier corresponding to each second height interval, and the state identifier and the state represented by the state identifier may be set based on an actual situation, which is not specifically limited in this application. Alternatively, the altitude interval with the state identifier 0 is in the unoccupied state, and the altitude interval with the state identifier 1 is in the occupied state.
Specifically, the state identifier corresponding to each first height interval in the first state identification information and the state identifier corresponding to the second height interval in the second state identification information are subjected to logic and processing, so that a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer can be obtained. For example, if the number of the first height sections and the number of the second height sections are 7, the first state identification information is 1010101, and the second state identification information is 0111101, the bits are logically and-processed for 1010101 and 0111101, and the result is 0010100, the state comparison results between the 7 second height sections and the corresponding first height sections are respectively state difference, both occupied states, and both occupied states.
S304, positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result.
And the movable platform positions the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform. Specifically, according to an online complete height layer and an offline complete height layer corresponding to each candidate positioning result, determining the matching degree corresponding to each candidate positioning result; and determining a second positioning result from the candidate positioning result set according to the matching degree corresponding to each candidate positioning result.
In one embodiment, the movable platform acquires a local offline complete height layer corresponding to each online complete height layer from the offline complete height layers; and determining the loss cost between each online complete height layer and the corresponding local offline complete height layer according to the height of each grid in each online complete height layer and the height of each grid in the corresponding local offline complete height layer, and taking the loss cost between each online complete height layer and the corresponding local offline complete height layer as the matching degree corresponding to each candidate positioning result.
The determination mode of the second positioning result is specifically as follows: the movable platform checks the candidate positioning results in the candidate positioning result set according to the matching degree corresponding to each candidate positioning result; and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as the second positioning result of the movable platform. The verification mode of the candidate positioning result is specifically as follows: and determining whether the matching degree of the candidate positioning result is less than or equal to a preset matching degree threshold, if the matching degree of the candidate positioning result is less than or equal to the preset matching degree threshold, determining that the candidate positioning result passes the verification, and if the matching degree of the candidate positioning result is greater than the preset matching degree threshold, determining that the candidate positioning result does not pass the verification. It should be noted that the matching degree threshold may be set based on actual situations, and this is not specifically limited in this application. And taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result of the movable platform, so that the accuracy of the positioning result can be further improved.
S305, determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
And after determining the first positioning result and the second positioning result of the movable platform, determining a target positioning result of the movable platform according to the first positioning result and the second positioning result. The accuracy and the stability of the positioning result can be further improved by fusing the two positioning results.
In one embodiment, the movable platform obtains the matching degree of the first positioning result and obtains the matching degree of the second positioning result; determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result; and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
The determination method of the first weight coefficient and the second weight coefficient specifically includes: normalizing the matching degree of the first positioning result and the matching degree of the second positioning result; determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result; determining a first weight coefficient of the first positioning result according to the matching degree and the total matching degree of the processed first positioning result; and determining a second weight coefficient of the second positioning result according to the matching degree and the total matching degree of the processed second positioning result.
The determination method of the weight coefficient specifically comprises the following steps: calculating the percentage of the matching degree of the processed first positioning result in the total matching degree, and taking the percentage as a first weight coefficient of the first positioning result; and calculating the percentage of the matching degree of the processed second positioning result in the total matching degree, and taking the percentage as a second weight coefficient of the second positioning result.
The determination mode of the target positioning result is specifically as follows: calculating the product of the first positioning result and the first weight coefficient to obtain a first weight positioning result; calculating the product of the second positioning result and the second weight coefficient to obtain a second weight positioning result; and calculating the sum of the first weight positioning result and the second weight positioning result, and taking the sum of the first weight positioning result and the second weight positioning result as the target positioning result of the movable platform.
The high-precision map positioning method provided by the above embodiment, through each candidate positioning result in the candidate positioning result set, performing rasterization processing on the online point cloud map to obtain a grid map corresponding to each candidate positioning result, with the offline non-ground height map layer and the online non-ground height map layer in each grid map, positioning the movable platform to obtain a positioning result, and simultaneously, by using the off-line complete height layer and the on-line complete height layer in each grid map, positioning the movable platform to obtain another positioning result, and finally determining the final positioning result of the movable platform through the two positioning results, the movable platform can be positioned under the scene that some features are sparse or lack of obvious features, and the accuracy and the stability of the high-precision map positioning result can be improved.
Referring to fig. 6, fig. 6 is a schematic flowchart illustrating steps of another high-precision map positioning method according to an embodiment of the present application.
Specifically, as shown in fig. 6, the high-precision map positioning method includes steps S401 to S407.
S401, obtaining an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises an offline complete height map layer and an offline non-ground height map layer.
The movable platform collects three-dimensional point cloud data of a driving area through a high-precision laser radar, and processes the collected three-dimensional point cloud data through a high-precision inertial navigation system and a point cloud registration algorithm to generate an off-line high-precision map.
The method comprises the steps that an off-line high-precision map is obtained by a movable platform in the moving process, three-dimensional point cloud data of objects around the movable platform are collected in real time through a laser radar, and an on-line point cloud map is established based on the three-dimensional point cloud data collected in real time.
The off-line high-precision map comprises an off-line complete height map layer and an off-line non-ground height map layer, wherein the height of each grid in the off-line complete height map layer is the mean height of three-dimensional point cloud in the grid, the off-line non-ground height map layer comprises a plurality of height intervals, the height intervals are obtained by dividing the three-dimensional point cloud according to the height of the three-dimensional point cloud, the height interval values of the height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height interval.
S402, obtaining a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment and the current moment are separated by preset time.
Specifically, a historical positioning result of the movable platform is obtained, wherein the historical positioning result is a positioning result determined by the movable platform at the previous moment, and the previous moment and the current moment are separated by a preset time. It should be noted that the preset time may be set based on actual situations, and the present application is not limited to this.
And S403, determining a candidate positioning result set according to the historical positioning result.
Specifically, historical position coordinates and historical attitude angles are obtained from the historical positioning result; performing derivation processing on the historical position coordinates to determine gradient values and gradient directions of the historical position coordinates, determining a candidate position set according to the gradient values and the gradient directions, namely obtaining a preset number of candidate gradient values along the gradient direction according to a preset unit gradient and determining position information corresponding to each candidate gradient value, thereby determining the candidate position set;
calculating a difference value between the historical attitude angle and a preset attitude error value, and calculating the sum of the historical attitude angle and the preset attitude error value; taking the difference value of the historical attitude angle and the preset attitude error value as an end point, taking the sum of the historical attitude angle and the preset attitude error value as another end point to obtain a candidate attitude angle range, and acquiring a plurality of candidate attitude angles from the candidate attitude angle range by using a preset unit attitude angle to form a candidate attitude set;
and determining a candidate positioning result set according to the candidate position set and the candidate posture set, namely, selecting a candidate position from the candidate position set and combining with each candidate posture in the candidate posture set each time, and collecting each candidate positioning result obtained by combination as the candidate positioning result set when the candidate positions in the candidate position set are all selected once.
It should be noted that the preset unit gradient, the preset number, the preset attitude error value, and the preset unit attitude angle may be set based on an actual situation, and this is not specifically limited in this application.
In an embodiment, the candidate position result set may be determined by: obtaining historical position coordinates and historical attitude angles from the historical positioning result, calculating the difference between the historical position coordinates and the historical attitude angles and a preset positioning error value respectively, calculating the sum between the historical position coordinates and the historical attitude angles and the preset positioning error value respectively, then determining a candidate coordinate set based on the difference sum between the historical position coordinates and the preset positioning error value, determining a candidate attitude set based on the difference sum between the historical attitude angles and the preset positioning error value, and finally determining a candidate positioning result set based on the candidate coordinate set and the candidate attitude set. It should be noted that the preset positioning error value may be set based on actual conditions, and this application is not limited to this specifically.
S404, according to each candidate positioning result in the candidate positioning result set, performing rasterization processing on the online point cloud map to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer.
The mobile platform performs rasterization processing on the online point cloud map according to each candidate positioning result to obtain an online raster map corresponding to each candidate positioning result, namely, each candidate positioning result in the candidate positioning result set is marked in the online point cloud map, and performs rasterization processing on a map area around each marked candidate positioning result to obtain a raster map corresponding to each candidate positioning result.
The online grid map comprises an online complete height map layer and an online non-ground height map layer, wherein the height of each grid in the online complete height map layer is the mean height of three-dimensional point cloud in the grid, the online non-ground height map layer comprises a plurality of height intervals, the height intervals are obtained by dividing the three-dimensional point cloud according to the height of the three-dimensional point cloud, the height interval values of the height intervals can be the same or different, and the height interval value is the height difference value of two end points of the height interval.
S405, positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result.
After the movable platform determines the online grid map corresponding to each candidate positioning result, the movable platform is positioned according to a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in the online grid map corresponding to each candidate positioning result, and a first positioning result of the movable platform is obtained. The movable platform is positioned through a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in each online grid map, so that an accurate positioning result can be obtained, and the accuracy and the stability of the positioning result of the high-precision map can be improved.
S406, positioning the movable platform according to the online complete height image layer and the offline complete height image layer corresponding to each candidate positioning result to obtain a second positioning result.
And the movable platform positions the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform. Specifically, according to an online complete height layer and an offline complete height layer corresponding to each candidate positioning result, determining the matching degree corresponding to each candidate positioning result; and determining a second positioning result from the candidate positioning result set according to the matching degree corresponding to each candidate positioning result.
S407, determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
And after determining the first positioning result and the second positioning result of the movable platform, determining a target positioning result of the movable platform according to the first positioning result and the second positioning result. The accuracy and the stability of the positioning result can be further improved by fusing the two positioning results.
In one embodiment, the movable platform obtains the matching degree of the first positioning result and obtains the matching degree of the second positioning result; determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result; and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
The high-precision map positioning method provided in the foregoing embodiment may accurately determine a candidate positioning result set according to a historical positioning result and a positioning error value, perform rasterization on an online point cloud map based on each candidate positioning result in the accurate candidate positioning result set to obtain a grid map corresponding to each candidate positioning result, position a mobile platform according to an offline non-ground height map layer and an online non-ground height map layer in each grid map to obtain a positioning result, position the mobile platform according to an offline full height map layer and an online full height map layer in each grid map to obtain another positioning result, and finally determine a final positioning result of the mobile platform according to the two positioning results, so that the mobile platform may be positioned in scenes with sparse features or lacking obvious features, the accuracy and the stability of the high-precision map positioning result can be improved.
Referring to fig. 7, fig. 7 is a schematic block diagram of a driving system according to an embodiment of the present application. In one embodiment, the driving system includes an unmanned system and a manned system. Further, the driving system 500 includes a processor 501, a memory 502, and a laser radar 503, wherein the processor 501, the memory 502, and the laser radar 503 are connected by a bus 504, and the bus 504 is, for example, an I2C (Inter-integrated Circuit) bus.
Specifically, the Processor 501 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.
Specifically, the Memory 502 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk.
Specifically, the processor 501 and the memory 502 are computing platforms of a driving system, and the laser radar 303 may be an external device of the driving system or an internal component of the driving system, which is not specifically limited in this application.
Wherein the processor 501 is configured to run a computer program stored in the memory 502 and to implement the steps of the high precision map positioning method as described above when executing the computer program.
It should be noted that, as will be clearly understood by those skilled in the art, for convenience and brevity of description, the specific working process of the driving system described above may refer to the corresponding process in the foregoing embodiment of the high-precision map positioning method, and details are not described herein again.
Referring to fig. 8, fig. 8 is a schematic block diagram of a movable platform according to an embodiment of the present application. The movable platform 800 includes a processor 601, a memory 602, and a lidar 603, with the processor 801, the memory 602, and the lidar 603 being connected by a bus 604, such as an I2C (Inter-integrated Circuit) bus 604. The movable platform comprises a vehicle and an aircraft, the aircraft comprises an unmanned aircraft and a manned aircraft, the vehicle comprises a manned vehicle, an unmanned vehicle and the like, and the unmanned aircraft comprises a rotary wing type unmanned aircraft, such as a four-rotor unmanned aircraft, a six-rotor unmanned aircraft and an eight-rotor unmanned aircraft, or a fixed wing type unmanned aircraft, or a combination of a rotary wing type and a fixed wing type unmanned aircraft, which is not limited herein.
Specifically, the Processor 601 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.
Specifically, the Memory 602 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk.
Specifically, the processor 601 and the memory 602 are computing platforms of a driving system, and the laser radar 603 may be an external device of the driving system, or an internal component of the driving system, which is not specifically limited in this application.
Wherein the processor 601 is configured to run a computer program stored in the memory 602 and to implement the steps of the high precision map positioning method as described above when executing the computer program.
It should be noted that, as will be clearly understood by those skilled in the art, for convenience and brevity of description, the specific working process of the movable platform described above may refer to the corresponding process in the foregoing embodiment of the high-precision map positioning method, and details are not described herein again.
In an embodiment of the present application, a computer-readable storage medium is further provided, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the high-precision map positioning method provided in the foregoing embodiment.
The computer readable storage medium may be an internal storage unit of the driving system or the mobile platform according to any of the foregoing embodiments, for example, a hard disk or a memory of the driving system or the mobile platform. The computer readable storage medium may also be an external storage device of the driving system or the mobile platform, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the driving system or the mobile platform.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (76)

1. A high-precision map positioning method is characterized by comprising the following steps:
acquiring an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result to obtain a first positioning result of the movable platform.
2. The method according to claim 1, wherein the positioning a movable platform according to a plurality of first height intervals in the offline high-precision map and a plurality of second height intervals in the online grid map corresponding to each candidate positioning result to obtain the first positioning result of the movable platform comprises:
determining the matching degree of each candidate positioning result according to a plurality of first height intervals in the off-line high-precision map and a plurality of second height intervals in the on-line grid map corresponding to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as the first positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
3. A high accuracy map positioning method according to claim 2 wherein said offline high accuracy map comprises an offline non-ground level, said plurality of first height intervals being located on said offline non-ground level, said online grid map comprises an online non-ground level, said plurality of second height intervals being located on said online non-ground level; the determining the matching degree of each candidate positioning result according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result respectively comprises:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and determining the matching degree corresponding to each candidate positioning result according to the number of the second height intervals with the state comparison result as a preset state comparison result in each online non-ground height map layer.
4. The method according to claim 3, wherein the determining a status comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer comprises:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to the first state identification information and each second state identification information.
5. The high accuracy map positioning method of any one of claims 1 to 4, wherein the online grid map further comprises an online full height layer, and the offline high accuracy map further comprises an offline full height layer; after the positioning the movable platform according to the plurality of first height intervals in the offline high-precision map and the plurality of second height intervals in the online grid map corresponding to each candidate positioning result, and obtaining the first positioning result of the movable platform, the method further includes:
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform;
and fusing the first positioning result and the second positioning result to obtain a target positioning result of the movable platform.
6. The method according to claim 5, wherein the positioning the movable platform according to the online full height layer and the offline full height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform comprises:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as a second positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
7. A high accuracy map positioning method according to claim 6, wherein said selecting one candidate positioning result from said candidate positioning result set as the second positioning result of said movable platform according to the matching degree corresponding to each candidate positioning result comprises:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result of the movable platform.
8. The method according to claim 5, wherein the fusing the first positioning result and the second positioning result to obtain the target positioning result of the movable platform comprises:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
9. The method according to claim 8, wherein determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the degree of matching of the first positioning result and the degree of matching of the second positioning result comprises:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
10. The method according to any one of claims 1 to 4, wherein the determining a candidate positioning result set comprises:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
11. A high accuracy map positioning method according to claim 10, wherein said determining a set of candidate positions from said current position data comprises:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
12. A high accuracy map positioning method according to claim 10, wherein said determining a set of candidate poses from said current pose data and a preset pose error value comprises:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
13. The method according to any one of claims 1 to 4, wherein the determining a candidate positioning result set comprises:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
14. A high-precision map positioning method is characterized by comprising the following steps:
acquiring an offline high-precision map and establishing an online point cloud map, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
15. The method according to claim 14, wherein the positioning the movable platform according to the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result to obtain a first positioning result comprises:
determining the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result;
and determining a first positioning result from the candidate positioning result set according to the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
16. The method according to claim 15, wherein the off-line non-ground height map layer comprises a plurality of first height intervals, the on-line non-ground height map layer comprises a plurality of second height intervals, and the first height intervals are the same as the corresponding second height intervals; the determining the matching degree between the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result includes:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and taking each number as the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
17. The method according to claim 16, wherein the determining a status comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer comprises:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to each second state identification information and the first state identification information.
18. The method according to claim 14, wherein the positioning the movable platform according to the online full height layer and the offline full height layer corresponding to each candidate positioning result to obtain a second positioning result includes:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and determining a second positioning result from the candidate positioning result set according to the matching degree corresponding to each candidate positioning result.
19. A method as claimed in claim 18, wherein said determining a second position result from said set of candidate position results according to the matching degree corresponding to each candidate position result comprises:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result.
20. The method according to any one of claims 14 to 19, wherein the determining a target positioning result of the movable platform according to the first positioning result and the second positioning result comprises:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
21. The method according to claim 20, wherein determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the degree of matching of the first positioning result and the degree of matching of the second positioning result comprises:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
22. The method according to any one of claims 14 to 19, wherein the determining a candidate positioning result set comprises:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
23. A high accuracy map positioning method according to claim 22, wherein said determining a set of candidate locations from said current location data comprises:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
24. A high accuracy map positioning method according to claim 22, wherein said determining a set of candidate poses from said current pose data and preset pose error values comprises:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
25. The method according to any one of claims 14 to 19, wherein the determining a candidate positioning result set comprises:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
26. A driving system, comprising a lidar, a memory, and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, to obtain a first positioning result of the movable platform.
27. The driving system according to claim 26, wherein the processor is configured to perform positioning on the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map corresponding to each candidate positioning result, and when obtaining the first positioning result of the movable platform, perform:
determining the matching degree of each candidate positioning result according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as the first positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
28. The driving system as defined in claim 27, wherein the offline high-precision map comprises an offline non-ground height floor, the plurality of first height intervals being located on the offline non-ground height floor, the online grid map comprises an online non-ground height floor, and the plurality of second height intervals being located on the online non-ground height floor; the processor is used for determining the matching degree corresponding to each candidate positioning result according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map corresponding to each candidate positioning result, and is used for realizing that:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and determining the matching degree corresponding to each candidate positioning result according to the number of the second height intervals with the state comparison result as a preset state comparison result in each online non-ground height map layer.
29. The driving system according to claim 27, wherein said processor, when effecting a state comparison between each of said second altitude intervals in each of said online non-ground altitude map layers and said corresponding first altitude interval in said offline non-ground altitude map layer, is configured to effect:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to the first state identification information and each second state identification information.
30. The driving system according to any one of claims 26 to 29, wherein the online grid map further comprises an online full height layer, and the offline high precision map further comprises an offline full height layer; the processor is used for positioning the movable platform according to a plurality of second height intervals in the online grid map and a plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, and obtaining a first positioning result of the movable platform, and is further used for realizing that:
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform;
and fusing the first positioning result and the second positioning result to obtain a target positioning result of the movable platform.
31. The driving system according to claim 30, wherein the processor is configured to implement, when obtaining a second positioning result of the movable platform, positioning the movable platform according to the online full height layer and the offline full height layer corresponding to each candidate positioning result, to implement:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as a second positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
32. The driving system as recited in claim 31, wherein said processor is configured to, when selecting one of said candidate position fixes from said set of candidate position fixes as said second position fix for said movable platform based on a respective degree of match for each of said candidate position fixes, perform:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result of the movable platform.
33. The steering system of claim 30, wherein the processor is configured to fuse the first positioning result and the second positioning result to obtain a target positioning result of the movable platform, and is configured to:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
34. The driving system of claim 33, wherein the processor is configured to determine a first weighting factor for the first positioning result and a second weighting factor for the second positioning result based on the degree of matching of the first positioning result and the degree of matching of the second positioning result, and further configured to:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
35. The driving system as defined in any one of claims 26 to 29, wherein the processor enables determining a set of candidate position results for enabling:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
36. The driving system as defined in claim 35, wherein the processor enables determining a set of candidate locations from the current location data for enabling:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
37. The driving system as recited in claim 35, wherein said determining a set of candidate poses based on said current pose data and a preset pose error value is configured to:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
38. The driving system as defined in any one of claims 26 to 29, wherein the processor enables determining a set of candidate position results for enabling:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
39. A driving system, comprising a lidar, a memory, and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
40. The driving system according to claim 39, wherein the processor is configured to perform positioning on the movable platform according to the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result to obtain a first positioning result, and configured to perform:
determining the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result;
and determining a first positioning result from the candidate positioning result set according to the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
41. The steering system of claim 40, wherein the offline non-ground height map layer includes a plurality of first height intervals, and the online non-ground height map layer includes a plurality of second height intervals, the first height intervals being the same as the corresponding second height intervals; the processor determines the matching degree between the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result, and is used for realizing:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and taking each number as the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
42. The driving system according to claim 41, wherein said processor enables determining a status comparison between each of said second altitude intervals in each of said online non-ground altitude map layers and said corresponding first altitude interval in said offline non-ground altitude map layer for enabling:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to each second state identification information and the first state identification information.
43. The driving system according to claim 39, wherein the processor is configured to perform positioning on the movable platform according to the online full height map layer and the offline full height map layer corresponding to each candidate positioning result, to obtain a second positioning result, and configured to perform:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and determining a second positioning result from the candidate positioning result set according to the matching degree corresponding to each candidate positioning result.
44. The driving system as defined in claim 43, wherein the processor is configured to determine a second positioning result from the set of candidate positioning results according to a matching degree corresponding to each candidate positioning result, and is configured to:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result.
45. The steering system of any one of claims 39 to 44, wherein the processor enables determining a target positioning result for the movable platform based on the first positioning result and the second positioning result for enabling:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
46. The driving system according to claim 45, wherein the processor enables determining a first weighting factor of the first positioning result and a second weighting factor of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result, for enabling:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
47. The driving system as defined in any one of claims 39 to 44, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
48. The driving system as defined in claim 47, wherein the processor enables determining a set of candidate locations from the current location data for enabling:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
49. The driving system as defined in claim 47, wherein the processor enables determination of a set of candidate poses from the current pose data and preset pose error values for enabling:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
50. The driving system as defined in any one of claims 39 to 44, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
51. A movable platform, comprising a lidar, a memory, and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises a plurality of first height intervals;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises a plurality of second height intervals;
and positioning the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, to obtain a first positioning result of the movable platform.
52. The movable platform of claim 51, wherein the processor is configured to perform positioning on the movable platform according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map corresponding to each candidate positioning result, and when obtaining the first positioning result of the movable platform, to perform:
determining the matching degree of each candidate positioning result according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as the first positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
53. The movable platform of claim 52, wherein the offline high-precision map comprises an offline non-ground height layer, the plurality of first height intervals are located on the offline non-ground height layer, the online grid map comprises an online non-ground height layer, and the plurality of second height intervals are located on the online non-ground height layer; the processor is used for determining the matching degree corresponding to each candidate positioning result according to the plurality of second height intervals in the online grid map and the plurality of first height intervals in the offline high-precision map corresponding to each candidate positioning result, and is used for realizing that:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and determining the matching degree corresponding to each candidate positioning result according to the number of the second height intervals with the state comparison result as a preset state comparison result in each online non-ground height map layer.
54. The movable platform of claim 53, wherein the processor, when enabled to determine a status comparison between each of the second height intervals in each of the online non-ground height layers and the corresponding first height interval in the offline non-ground height layer, is configured to enable:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to the first state identification information and each second state identification information.
55. The movable platform of any one of claims 51-54, wherein the online grid map further comprises an online full height layer, and the offline high precision map further comprises an offline full height layer; the processor is used for positioning the movable platform according to a plurality of second height intervals in the online grid map and a plurality of first height intervals in the offline high-precision map, which correspond to each candidate positioning result, and obtaining a first positioning result of the movable platform, and is further used for realizing that:
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result of the movable platform;
and fusing the first positioning result and the second positioning result to obtain a target positioning result of the movable platform.
56. The movable platform of claim 55, wherein the processor is configured to implement, when obtaining the second positioning result of the movable platform, positioning the movable platform according to the online full height layer and the offline full height layer corresponding to each candidate positioning result, to implement:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and selecting one candidate positioning result from the candidate positioning result set as a second positioning result of the movable platform according to the matching degree corresponding to each candidate positioning result.
57. The movable platform of claim 56, wherein the processor is configured to select one of the candidate position fixes from the set of candidate position fixes as the second position fix of the movable platform according to a respective matching degree of each candidate position fix, and further configured to:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result of the movable platform.
58. The movable platform of claim 55, wherein the processor is configured to fuse the first positioning result and the second positioning result to obtain a target positioning result of the movable platform, and is configured to:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
59. The movable platform of claim 58, wherein the processor is configured to determine a first weighting factor for the first positioning result and a second weighting factor for the second positioning result based on the degree of matching of the first positioning result and the degree of matching of the second positioning result, and further configured to:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
60. The movable platform of any one of claims 51-54, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
61. The movable platform of claim 60, wherein the processor enables determining a set of candidate locations from the current location data for enabling:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
62. The movable platform of claim 60, wherein the set of candidate poses is determined from the current pose data and a preset pose error value for implementing:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
63. The movable platform of any one of claims 51-54, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
64. A movable platform, comprising a lidar, a memory, and a processor;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and, when executing the computer program, implement the following steps:
acquiring an offline high-precision map, and establishing an online point cloud map through three-dimensional point cloud data acquired by the laser radar, wherein the offline high-precision map comprises an offline complete height layer and an offline non-ground height layer;
determining a candidate positioning result set, and rasterizing the online point cloud map according to each candidate positioning result in the candidate positioning result set to obtain an online grid map corresponding to each candidate positioning result, wherein the online grid map comprises an online complete height map layer and an online non-ground height map layer;
positioning the movable platform according to the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result to obtain a first positioning result; and
positioning the movable platform according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result to obtain a second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result and the second positioning result.
65. The movable platform of claim 64, wherein the processor is configured to perform positioning on the movable platform according to the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result to obtain a first positioning result, and configured to perform:
determining the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result;
and determining a first positioning result from the candidate positioning result set according to the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
66. The movable platform of claim 65, wherein the offline non-ground height map layer comprises a plurality of first height intervals, and the online non-ground height map layer comprises a plurality of second height intervals, the first height intervals being the same as the corresponding second height intervals; the processor determines the matching degree between the online non-ground height map layer and the offline non-ground height map layer corresponding to each candidate positioning result, and is used for realizing:
determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer;
counting the number of the second height intervals with the state comparison results as preset state comparison results in each online non-ground height map layer;
and taking each number as the matching degree between the online non-ground height image layer and the offline non-ground height image layer corresponding to each candidate positioning result.
67. The movable platform of claim 66, wherein the processor enables determining a status comparison between each of the second height intervals in each of the online non-ground height layers and the corresponding first height interval in the offline non-ground height layer to enable:
acquiring first state identification information of each first height interval in the off-line non-ground height layer and acquiring second state identification information of each second height interval of each on-line non-ground height layer;
and determining a state comparison result between each second height interval in each online non-ground height layer and the corresponding first height interval in the offline non-ground height layer according to each second state identification information and the first state identification information.
68. The movable platform of claim 64, wherein the processor is configured to perform positioning on the movable platform according to the online full height layer and the offline full height layer corresponding to each candidate positioning result, to obtain a second positioning result, and configured to perform:
determining the matching degree corresponding to each candidate positioning result according to the online complete height layer and the offline complete height layer corresponding to each candidate positioning result;
and determining a second positioning result from the candidate positioning result set according to the matching degree corresponding to each candidate positioning result.
69. The movable platform of claim 68, wherein the processor is configured to determine a second position fix from the set of candidate position fixes based on a respective degree of match for each candidate position fix, and is configured to:
according to the matching degree corresponding to each candidate positioning result, checking the candidate positioning results in the candidate positioning result set;
and acquiring the matching degree corresponding to each verified candidate positioning result, and taking the verified candidate positioning result corresponding to the minimum matching degree as a second positioning result.
70. The movable platform of any one of claims 64-69, wherein the processor enables determining a target positioning result for the movable platform based on the first positioning result and the second positioning result for enabling:
obtaining the matching degree of the first positioning result and the matching degree of the second positioning result;
determining a first weight coefficient of the first positioning result and a second weight coefficient of the second positioning result according to the matching degree of the first positioning result and the matching degree of the second positioning result;
and determining a target positioning result of the movable platform according to the first positioning result, the second positioning result, the first weight coefficient and the second weight coefficient.
71. The movable platform of claim 70, wherein the processor is configured to determine a first weighting factor of the first positioning result and a second weighting factor of the second positioning result according to a matching degree of the first positioning result and a matching degree of the second positioning result, and is configured to:
normalizing the matching degree of the first positioning result and the matching degree of the second positioning result;
determining the total matching degree according to the matching degree of the processed first positioning result and the matching degree of the processed second positioning result;
determining a first weight coefficient of the first positioning result according to the processed matching degree of the first positioning result and the total matching degree;
and determining a second weight coefficient of the second positioning result according to the processed matching degree of the second positioning result and the total matching degree.
72. The movable platform of any one of claims 64-69, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring current position data and current attitude data of the movable platform;
determining a candidate location set from the current location data;
determining a candidate attitude set according to the current attitude data and a preset attitude error value;
and determining a candidate positioning result set according to the candidate position set and the candidate attitude set.
73. The movable platform of claim 72, wherein the processor enables determining a set of candidate locations from the current location data for enabling:
and determining the change trend of the current position data, and determining a candidate position set according to the change trend of the current position data.
74. The movable platform of claim 72, wherein the processor enables determining a set of candidate poses based on the current pose data and a preset pose error value for enabling:
calculating a difference value between the attitude angle in the current attitude data and a preset attitude error value, and calculating a sum of the attitude angle in the current attitude data and the preset attitude error value;
and determining a candidate attitude set according to the difference value between the attitude angle in the current attitude data and a preset attitude error value and the sum of the attitude angle in the current attitude data and the preset attitude error value.
75. The movable platform of any one of claims 64-69, wherein the processor enables determining a set of candidate position fixes for enabling:
acquiring a historical positioning result of the movable platform, wherein the historical positioning result is a positioning result determined by the movable platform at the last moment, and the last moment is separated from the current moment by preset time;
and determining a candidate positioning result set according to the historical positioning result.
76. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the high accuracy map positioning method according to any one of claims 1 to 25.
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