CN115143977A - Rapid high-precision map construction method and device and vehicle - Google Patents

Abstract

The invention discloses a rapid high-precision map construction method, which comprises the following steps: the method comprises the following steps that S1, in an outdoor scene, whether GNSS signals of the outdoor scene are good or not is judged according to original measurement data of GNSS, and if yes, preset useful data in original measurement data of a plurality of sensors preset on an unmanned vehicle are continuously processed in a preset format; s2, generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to the preset useful data of the sensor processed in the step S1; step S3, processing the driving track of the unmanned vehicle obtained in the step S2 and the marked special function points or special task function areas to generate a high-precision map; and S4, carrying out integrity detection on the high-precision map to obtain the complete high-precision map. The method can effectively improve the construction efficiency of the high-precision map and simultaneously improve the utilization rate and the adaptability of the unmanned vehicle to the GNSS signals.

Description

Rapid high-precision map construction method and device and vehicle

Technical Field

The invention relates to the technical field of automatic driving and high-precision map construction, in particular to a rapid high-precision map construction method, a device and a vehicle thereof.

Background

The automatic driving technology is a hot topic in recent years, and in the fields of relieving traffic jam, improving road safety, reducing air pollution and the like, automatic driving brings subversive change.

In the automatic driving commercialization process, unmanned cleaning vehicles, unmanned express delivery vehicles, unmanned taxies and the like in a limited area scene provide a substantial application scene for landing of an automatic driving technology. The high-precision map can provide prior map information for the vehicle, so that the automatic driving technology based on the high-precision map is an optimal solution for realizing the automatic driving of the L4 and L5 levels in the industry.

With the increasingly prominent demand of the relevant industries on automatic driving, the automatic driving landing scene is clearer, especially under the outdoor scene with good GNSS (Global Navigation Satellite System) signals, the demands of vehicles such as unmanned environmental sanitation, unmanned distribution and the like are continuously improved, the construction efficiency, the quality and the precision of the automatic driving map are examined,

at present, in the automatic driving industry, a map construction method which is applied more is a method for constructing a point cloud or a grid map based on laser or visual data. The high-precision map construction method mainly comprises two strategies of off-line and on-line.

According to the strategy for constructing the high-precision map offline, relevant sensor data are collected through collection equipment such as vehicles, the offline data are transmitted to the local or cloud end, and finally the construction of the high-precision map is completed at the local or cloud end. In a strategy for constructing a high-precision map offline, complicated processes such as uploading and downloading of original data, backup and issuing of result data and the like are involved, manual intervention is more, the automation degree is low, the efficiency is reduced, and waste of manual resources and local computing resources is seriously caused.

And the strategy for constructing the high-precision map on line comprises the steps of firstly obtaining relevant sensor data of the vehicle in real time, obtaining a local map by calculating the sensor data, secondly optimizing the map in real time according to a set strategy, and finally obtaining the high-precision map with global consistency. For the strategy of constructing a high-precision map on line, because point cloud or grid map construction tasks are completed in the vehicle-mounted processor, the requirement on the performance of the point cloud or grid map construction tasks is high, a large map is difficult to process, and the quality and the error of the map cannot be guaranteed.

For the existing method for constructing a point cloud or grid map based on laser or visual data, the construction effect of a high-precision map is not friendly in the scenes with good GNSS signals such as squares. Under the condition that the GNSS signal is good and simple, the method has complex construction process, long time consumption and large resource consumption on the point cloud or grid map.

Disclosure of Invention

The invention aims to provide a rapid high-precision map construction method, a device and a vehicle thereof aiming at the technical defects in the prior art.

Therefore, the invention provides a rapid high-precision map construction method, which comprises the following steps:

the method comprises the following steps that S1, in an outdoor scene, original measurement data of a plurality of preset sensors including GNSS installed on an unmanned vehicle are obtained, whether GNSS signals of the outdoor scene are good or not is judged according to the original measurement data of the GNSS, and if yes, preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle are continuously processed through preset format processing;

step S2, generating a driving track of the unmanned vehicle and marking a special function point or a special task function area according to the preset useful data of the plurality of preset sensors processed in the step S1;

step S3, processing the driving track of the unmanned vehicle obtained in the step S2 and the marked special function points or special task function areas to generate a high-precision map;

and S4, carrying out integrity detection on the high-precision map generated in the step S3, and finally obtaining the complete high-precision map.

Preferably, step S1 comprises in particular the following sub-steps:

step S11, acquiring original measurement data of a plurality of preset sensors including GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether GNSS signals of the outdoor scene are good or not according to the original measurement data of the GNSS, and if so, continuing to execute the step S12;

wherein, a plurality of sensors are preset, including GNSS, IMU, and wheel speed meters;

and S12, according to the format requirement of the EKF fusion algorithm of the extended Kalman filter, analyzing the original measurement data in the step S11 into a corresponding specific format, and continuously executing the step S2.

Preferably, in step S11, the GNSS is configured to provide raw GNSS signal measurement data of the unmanned vehicle, and specifically includes: latitude and longitude, course of GNSS signal the speed in the north east direction the number of stars collected, the state, the heading flag bit and the horizontal precision factor;

the IMU is used for providing IMU raw measurement data of the unmanned vehicle, and specifically comprises the following steps: acceleration and angular velocity in x, y and z directions under the IMU coordinate system;

the wheel speed meter is used for providing wheel speed raw measurement data of the unmanned vehicle, and specifically comprises left wheel speed and right wheel speed of the unmanned vehicle;

in step S12, useful data are preset in the raw measurement data of step S11, specifically including latitude and longitude, heading, speed in the northeast direction in the GNSS signal measured by the GNSS, acceleration and angular speed in the x, y and z directions measured by the inertial measurement unit IMU, and left wheel speed and right wheel speed of the unmanned vehicle measured by the wheel speed meter;

in step S11, when the state of the GNSS signal, the number of stars collected, the horizontal accuracy factor, and the heading flag meet the requirement and reach or exceed the set threshold, it is determined that the GNSS signal of the outdoor scene is good.

Preferably, step S2 comprises in particular the following sub-steps:

step S21, calculating the change amount of the pose of the unmanned vehicle in a preset period according to the preset useful data of the inertial measurement unit IMU and the preset useful data of the wheel speed meter processed in the step S1, and calculating the latest predicted pose of the unmanned vehicle after the preset period by combining the unmanned vehicle fusion pose calculated in the last preset period by the Global Navigation Satellite System (GNSS);

step S22, processing the preset useful data of the GNSS processed in the step S1, the latest predicted pose of the unmanned vehicle obtained in the step S21, the pose information calculated and provided by the inertial measurement unit IMU and the left wheel speed and the right wheel speed of the unmanned vehicle measured by the wheel speed meter by an extended Kalman filtering EKF fusion algorithm to obtain high-precision fused pose information of the unmanned vehicle and the running track of the unmanned vehicle;

the fusion pose information comprises fusion position information and fusion attitude information;

and step S23, judging whether the current outdoor scene is matched completely, if not, returning to execute the step S1, and if the scene is matched, continuing to execute the step S3, namely, inputting each item of stored and marked data to the following step S3.

Preferably, in step S22, the method also includes the steps of: marking a special function point or a special task function area according to the requirement of an operation user, and specifically comprising the following processing steps:

step S220, detecting whether an operator has a marking requirement of inputting a special function point or a special task function area to the fusion position or not in real time according to the fusion position information of the unmanned vehicle subjected to fusion processing, if so, correspondingly marking the special function point or the special task function area on the fusion position, modifying the function attribute information of the special function point or the special task function area, and if not, storing the pose information of the unmanned vehicle subjected to fusion processing in real time.

Preferably, step S3 specifically comprises the following sub-steps:

step S31, expanding the driving track of the unmanned vehicle obtained in the step S2 outwards by a preset distance value K to obtain a passable area where the unmanned vehicle drives under the current outdoor scene, and storing the passable area in a specific format;

in step S31, for different outdoor scenes, there are corresponding preset distance values K respectively;

step S32, generating corresponding position information of the special function point or function area boundary and passable area boundary of the special task function area according to the special function point or function attribute information of the special task function area marked in the step S2;

and step S33, according to the existing zigzag strategy or global path planning strategy, according to the corresponding relation between the special function points and the special task function areas, the passable area boundary and the special task function area boundary information, automatically generating a reference path of the unmanned vehicle in normal operation, according to the corresponding relation between the special function points and the special task function areas, generating all associated operation tasks in the current outdoor scene, and then storing the operation tasks, thereby obtaining a high-precision map.

Preferably, step S4 specifically comprises the following sub-steps:

step S41, detecting whether the file of the high-precision map generated in the step S3 is complete, if so, continuing to execute the step S42, and if not, returning to execute the step S1;

and S42, detecting whether all preset high-precision necessary elements in the high-precision map generated in the step S3 are complete, if so, taking the high-precision map generated in the step S3 as a high-precision map which is finally issued outwards, and if not, returning to execute the step S1.

Preferably, in step S42, preset high-precision necessary elements including a special function point, a special task function area, a reference path, and a task;

detecting whether all preset high-precision necessary elements in the high-precision map generated in the step S3 are complete, specifically: detecting whether three elements, namely a special function point, a special task function area and a reference path, exist or not, and if so, considering the three elements to be complete; and for the task, detecting whether the reference paths corresponding to the operation task are connected or not, if so, considering the task to be complete, otherwise, considering the task to be incomplete.

In addition, the invention also provides a quick high-precision map construction device, which comprises the following modules:

the sensor data preprocessing module is used for acquiring original measurement data of a plurality of preset sensors including a GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether GNSS signals of the outdoor scene are good or not according to the original measurement data of the GNSS (global navigation satellite system), and if so, continuously performing preset format processing operation on preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle;

the vehicle track and function information processing module is connected with the sensor data preprocessing module and is used for generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to preset useful data of a plurality of preset sensors processed by the sensor data preprocessing module;

the high-precision map processing module is connected with the vehicle track and function information processing module and used for processing the running track of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle track and function information processing module to generate a high-precision map and then sending the high-precision map to the high-precision map processing module;

and the map integrity detection module is connected with the high-precision map processing module and is used for carrying out integrity detection on the high-precision map generated by the high-precision map processing module so as to finally obtain the complete high-precision map.

In addition, the invention also provides a vehicle which comprises the rapid high-precision map constructing device.

Compared with the prior art, the rapid high-precision map construction method, the rapid high-precision map construction device and the rapid high-precision map construction vehicle are scientific in design, can effectively improve the construction efficiency of the high-precision map, and simultaneously improve the utilization rate and adaptability of unmanned vehicles to GNSS signals, especially the adaptability to environments such as large squares and the like, and have great practical significance.

The invention is a map construction method with high efficiency, low consumption and high precision, has scientific and easily realized design principle, clear logic and good scene adaptability to GNSS signals, meets the technical requirements of unmanned vehicles on construction efficiency, calculation resource consumption and precision of high-precision scene maps, is beneficial to ensuring the safety of automatic driving vehicles, quickens the commercialized landing pace of the unmanned vehicles and ensures reliable commercialized landing.

Drawings

FIG. 1 is a main flow chart of a fast high-precision map construction method according to the present invention;

FIG. 2 is a flowchart illustrating the overall operation of a fast high-precision map construction method according to the present invention;

fig. 3 is a schematic view of a passable area of an unmanned vehicle in an outdoor scene obtained by applying the present invention.

Detailed Description

In order that those skilled in the art will better understand the technical solution of the present invention, the following detailed description of the present invention is provided in conjunction with the accompanying drawings and embodiments.

Referring to fig. 1 and 2, the invention provides a fast high-precision map construction method, which comprises the following steps:

step S1, acquiring original measurement data of a plurality of preset sensors including a GNSS (global navigation satellite system) installed on an unmanned vehicle in an outdoor scene, judging whether a GNSS signal of the outdoor scene is good or not according to the original measurement data of the GNSS (global navigation satellite system), and if so, continuing to perform preset format processing operation on preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle;

it should be noted that, if the GNSS signal is not good, the next operation is not performed, and at this time, the existing high-precision map building method may be selected and used.

In the present invention, step S1 specifically includes the following substeps:

step S11, acquiring original measurement data of a plurality of preset sensors including a GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether a GNSS signal in the outdoor scene is good or not according to the original measurement data of the GNSS (global navigation satellite system), if so, continuing to execute step S12, otherwise, not continuing to execute;

the method comprises the steps that a plurality of sensors are preset, wherein the sensors comprise a GNSS (global navigation satellite system), an IMU (Inertial measurement unit) and a wheel speed meter;

it should be noted that, in scenes such as squares with good GNSS signals, the good GNSS signals mean: the state, the satellite receiving number, the horizontal accuracy factor and the heading flag bit of the GNSS signal meet the requirements and reach or exceed the set threshold value.

In step S11, when the GNSS signal state, the number of stars collected, the horizontal accuracy factor, and the heading flag meet the requirements and reach or exceed the set threshold, it is determined that the GNSS signal of the outdoor scene is good.

It should be noted that GNSS (i.e., global navigation satellite system) is a space-based radio navigation positioning system that can provide users with all-weather three-dimensional coordinates and speed and time information at any location on the earth's surface or in the near-earth space.

In the present invention, in step S11, the global navigation satellite system GNSS is configured to provide raw measurement data of GNSS signals of an unmanned vehicle, and specifically includes: the latitude and longitude of the GNSS signal, the course, the speed in the northeast direction, the number of stars collected, the state, the course flag bit and the horizontal accuracy factor.

It should be noted that the number of satellites, the state, the heading flag bit, and the horizontal accuracy factor of the GNSS signal are evaluation parameters for determining whether the GNSS signal is good or not.

The inertial measurement unit IMU is used for providing IMU raw measurement data of the unmanned vehicle, and specifically comprises: acceleration and angular velocity in x, y and z directions under the IMU coordinate system;

the wheel speed meter is used for providing raw wheel speed measurement data of the unmanned vehicle, and specifically comprises left wheel speed and right wheel speed of the unmanned vehicle.

Step S12, according to the format requirement of the EKF (Extended Kalman Filter) fusion algorithm, analyzing the original measurement data in step S11 into a corresponding specific format (for example, formats such as png and xml), and continuing to execute step S2.

In step S12, useful data are preset in the raw measurement data of step S11, specifically including latitude and longitude, heading, speed in the northeast direction in the GNSS signals measured by the GNSS, acceleration and angular speed in three directions (x, y, z) measured by the inertial measurement unit IMU, and left and right wheel speeds of the unmanned vehicle measured by the wheel speed meter.

It should be noted that, for the present invention, the data storage is not involved in S1 — after the required useful data is extracted from the raw measurement data of each sensor, the obtained data amount is small, and the single frame sensor data is processed in real time, so that the data can be directly stored in the buffer. The analysis mode is also simple, the preset useful data needed in each sensor is directly extracted and stored in a defined data structure (such as formats of png, xml and the like), and therefore the data can be input to the next step for operation.

S2, generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to the preset useful data of the plurality of preset sensors processed in the step S1;

in the present invention, step S2 specifically includes the following substeps:

step S21, calculating the variation of the pose (including the position and the attitude) of the unmanned vehicle in a preset period (namely a period of preset length) according to the preset useful data of the inertial measurement unit IMU and the preset useful data of the wheel speed meter processed in the step S1, and calculating the latest predicted pose (including the position and the attitude) of the unmanned vehicle after the unmanned vehicle is in the preset period by combining the unmanned vehicle fusion pose calculated by the global navigation satellite system GNSS in the last preset period (specifically, a certain moment in the period);

it should be noted that, in step S21, the change amount of the pose of the unmanned vehicle in the preset period (i.e., a period of the preset length) specifically includes: variations in east speed and position, variations in north speed and position, variations in sky speed and position, and variations in roll angle (roll), pitch angle (pitch), and yaw angle (yaw).

In step S21, the preset period is related to the vehicle speed, the preset period is in inverse proportion to the vehicle speed, the higher the vehicle speed is, the shorter the period is, and the T (T is less than or equal to 0.01 ms) is recommended to be the preset period according to the average speed of the passenger vehicle

In step S21, the change amount of the pose of the unmanned vehicle in the preset period (i.e. a period of the preset length) is calculated according to the preset useful data of the inertial measurement unit IMU and the preset useful data of the wheel speed meter processed in step S1, and this calculation processing operation is common technical knowledge and adopts a conventional calculation means, which is not described herein again. For example, if the wheel speed meter measures speed, the speed is integrated over a period to obtain the position change; and integrating the angular velocity of the IMU to obtain an angle variation, integrating the acceleration for the first time to obtain a velocity variation, and integrating for the second time to obtain a position variation.

It should be noted that, in step S21, the algorithm of the present invention operates in a strict period system, which is also the preset period T mentioned above, and can ensure that the frequency of the fused pose output is fixed and unchanged. In a specific implementation, the unmanned vehicle fusion pose calculated by the GNSS in the previous preset period is obtained at a time point (moment) in the previous preset period T, and the fusion pose is output at the time point.

It should be noted that the pose provided by the GNSS at each time is an absolute position pose. The latest predicted pose after the preset period can be obtained by adding the pose variation of the unmanned vehicle in the preset period (namely a period of the preset length) and the unmanned vehicle fusion pose calculated by the Global Navigation Satellite System (GNSS) in the last preset period.

Step S22, processing the preset useful data of the global navigation satellite system GNSS processed in the step S1, the latest predicted pose of the unmanned vehicle obtained in the step S21, the pose information calculated and provided by the inertial measurement unit IMU and the left wheel speed and the right wheel speed of the unmanned vehicle measured by the wheel speed meter by an EKF (Extended Kalman Filter) fusion algorithm to obtain high-precision fusion pose information (including position and pose) of the unmanned vehicle and the running track of the unmanned vehicle after fusion processing; the fusion pose information comprises fusion position information and fusion attitude information;

it should be noted that, the EKF (Extended Kalman Filter) fusion algorithm is a well-known algorithm at present, is a well-established technology at present, and has the functions of: and (4) fusing data of all the input (GNSS, IMU and wheel speed meter) to obtain the final high-precision pose of the unmanned vehicle at the moment.

In the present invention, in step S22, a travel locus of the unmanned vehicle can be formed in time series by storing the fusion poses of the unmanned vehicle at each time.

In the present invention, in step S22, the method also includes the steps of: and marking the special function points or the special task function areas according to the requirements of the operation users. The method specifically comprises the following processing steps:

step S220, detecting whether an operator has a marking requirement for inputting a special function point or a special task function area to the fusion position in real time for the fusion position information of the unmanned vehicle subjected to fusion processing, if so, correspondingly marking the special function point or the special task function area on the fusion position, modifying the function attribute information (namely specific function information) of the special function point or the special task function area, and if not, storing the pose information (comprising position and posture) of the unmanned vehicle subjected to fusion processing in real time.

Note that, the special function points: the vehicle-mounted intelligent parking system is a position and pose point (similar to a bus stop) with special functions, such as a departure point, a midway parking point and a terminal point of a passenger car. The special function point is that a client selects and issues the function point attribute through an interactive page button, the algorithm can detect whether the command is issued at the end of each period, if so, the fusion pose and the function point attribute (namely, the specific function is provided or exerted) at the moment are recorded.

Special task functional area: the method is an area with special functions, such as a speed limit area of a passenger car, a cleaning area of a sanitation car, a pedestrian lane, an intersection, an ascending and descending slope, a special mark area and the like on a road, and the implementation method and the function points are the same, but the protocols are different.

Step S23, determining whether the current outdoor scene is completely adapted, if not, returning to execute step S1, and if the scene is completely adapted, continuing to execute step S3, that is, inputting the stored and marked data to the following step S3.

In the present invention, in step S23, the following are adapted: refers to the entire process of constructing a high-precision map of the unmanned vehicle operating environment.

In step S23, it is judged that the adaptation is completed: and (3) designating a corresponding protocol, issuing and finishing the adaptation process by a client through a related button in the interactive page, and detecting whether an issuing and finishing command exists or not after each period of the algorithm is finished.

That is, in step S23, it is determined whether the current outdoor scene is completely adapted, specifically: when an adaptation ending instruction input by a client (user) is received, the outdoor scene adaptation is judged to be completed. If the adaptation ending instruction input by the client (user) is not received, the outdoor scene is judged not to be adapted completely.

Step S3, processing the driving track of the unmanned vehicle obtained in the step S2 and the marked special function points or special task function areas to generate a high-precision map;

in the present invention, step S3 specifically includes the following substeps:

step S31, extending the driving trajectory of the unmanned vehicle obtained in step S2 outward (for example, in each direction of a three-dimensional space) by a preset distance value (i.e., a K value, where the K value is an average distance value from a running route of the unmanned vehicle to a scene boundary when the current outdoor scene is adapted, and is a fixed value of an adaptation constraint), to obtain a passable area where the unmanned vehicle is driven in the current outdoor scene, and storing the passable area in a specific format (for example, formats such as png and xml);

in the present invention, in step S31, the preset distance value K is an empirical value, for example, if the adaptive scene is a standard urban road, the preset value is a plurality of lane width values, if the adaptive scene is a square scene, the preset value is a plurality of vehicle width values, if the adaptive scene is a park scene, the preset value is a half of the road width, and the like, and different fixed values (one-to-one correspondence is a constraint) are selected according to different scenes. For example, as shown in fig. 3. Fig. 3 is a schematic view of a passable area of an unmanned vehicle in an outdoor scene obtained by applying the present invention.

In step S31, there are corresponding preset distance values K for different outdoor scenes.

In step S31, the driving trajectory of the unmanned vehicle obtained in step S2 is expanded outward (for example, in each direction of the three-dimensional space) by a preset distance value, specifically: referring to fig. 3, a square region having a distance of K is expanded outward in a cross-shaped manner (i.e., four directions of right front, right back, right left, and right) with a fusion position in a driving track as a center.

In the present invention, in step S31, the passable area: the area where the vehicle can run and the area where the vehicle cannot run are divided into high-precision map boundaries, and the high-precision map boundaries can be understood as electronic fences. The region is expanded outward in a cross shape, all the fusion positions are completely expanded, and the region is a passable region in the region and the region which is not in the expansion region is a non-passable region.

And S32, generating position information of the corresponding special function point or function area boundary and passable area boundary of the special task function area according to the function attribute information (specifically marked on the fusion position of the unmanned vehicle) of the special function point or the special task function area marked in the step S2.

It should be noted that the passable area boundary includes a functional area boundary.

It should be noted that the functional point location information includes: acceleration and angular velocity of three direction (x, y, z), and roll angle (roll), pitch angle (pitch) and yaw angle (yaw), the effect of function point position information is: and the unmanned vehicle is ensured to meet the requirement of the vehicle position and posture.

Functional area boundary: the function is that the curve set is composed of a plurality of position points only containing three directions (x, y and z): for normalizing the vehicle to perform different operations or tasks in different areas, such as speed reduction, speed limit, steering, etc.

In the invention, the acquisition mode of the functional area boundary of the special task functional area is the same as the acquisition mode of the functional point and the functional area, but the protocol is different, the acquisition mode comprises a starting point and an end point, and the starting point and the end point of the functional area appear in pairs.

In the present invention, as shown in fig. 3, the boundary curve can be obtained by extending outward in a cross-shaped manner (i.e., four directions of right front, right back, right left, and right), where the extended link is between the start point and the end point of the functional area, and the generated functional area boundaries are simply and smoothly connected.

In the present invention, in step S32, it should be noted that only some types of function points are in the function area of the special task, and such function points need to automatically generate corresponding tasks according to the formulated task mode rule (providing a global relationship between the start and end points of the tasks for the downstream decision planning).

In the present invention, in step S32, it should be noted that the special function point is necessarily the start point or the end point of the special task function area, which is the corresponding relationship between the special function point and the special task function area.

Step S33, according to the existing zigzag strategy or global path planning strategy, according to the corresponding relation between the special function points and the special task function areas, the passable area boundary and the special task function area boundary information, a reference path of the unmanned vehicle in normal operation is automatically generated, and according to the corresponding relation between the special function points and the special task function areas (namely the special function points are the starting point or the end point of the special task function areas), all associated (corresponding relation exists) operation tasks in the current outdoor scene are generated and then stored, so that a high-precision map is obtained.

In step S33, it should be noted that the zigzag shape is a method for automatically generating a full-coverage reference path by performing full-coverage cleaning on the cleaning area of the sweeper truck, and is a conventional method for automatically generating a full-coverage reference path. The global path planning is for the passenger car, and only needs to pass through the road without executing various special tasks.

It should be noted that, due to the limitation of vehicle hardware, the vehicle has a minimum turning radius, and therefore, in step S33, the curve radius of the turning point of the reference path during normal operation of the unmanned vehicle needs to be larger than the minimum turning radius of the unmanned vehicle.

It should be noted that, in step S33, regarding the reference route of the unmanned vehicle during normal operation, the unmanned vehicle needs to travel along the reference route

In step S33, it should be noted that, based on the existing zigzag strategy or global path planning strategy (for example, a-algorithm), the reference path of the unmanned vehicle during normal operation can be automatically generated according to the correspondence between the special function points and the special task function areas, the passable area boundary, and the information about the special task function area boundary. This is a well-known technique and will not be described herein.

In step S33, regarding all possible operation tasks in the current outdoor scenario, refer to: for the reference route generated by the corresponding relation between the special function points and the special task function areas, the customer can select the specified task to operate through the interactive interface.

According to the invention, a plurality of correlated reference paths are generated according to the corresponding relation between the special function points and the special task function areas, each correlated reference path is an operation task, and all correlated operation tasks are combined.

S4, carrying out integrity detection on the high-precision map generated in the step S3 to finally obtain an integral high-precision map;

in the present invention, step S4 specifically includes the following substeps:

step S41, detecting whether the file of the high-precision map generated in step S3 is complete, if yes, continuing to execute the step S42, and if not, returning to execute the step S1;

in step S41, it is detected whether the file of the high-precision map generated in step S3 is complete, specifically: it is checked in step S3 whether all folders are generated (i.e. whether there is a problem that no folders are generated), and whether all result files are generated (i.e. whether there is a problem that no result files are generated). Without this detection step, the integrity of high accuracy cannot be guaranteed, and automatic driving cannot be achieved.

And step S42, detecting whether preset high-precision necessary elements in the high-precision map generated in the step S3 are complete, if so, taking the high-precision map generated in the step S3 as a high-precision map which is finally issued outwards, and if not, returning to execute the step S1.

In step S42, the high-precision necessary elements are preset, including the special function point, the special task function area, the reference path, and the task.

Wherein, whether the preset high-precision necessary elements are complete or not in the high-precision map generated in the detection step S3 is specifically: detecting whether three elements, namely a special function point, a special task function area and a reference path, exist or not, and if so, considering the three elements to be complete; and for the task, whether a reference path corresponding to the operation task (for example, the main operation task selected and input by the customer) is connected (that is, not disconnected) is detected, if so, the task is considered to be complete, otherwise, the task is considered to be incomplete.

It should be noted that, in the present invention, if one of the two detections in step S41 and step S42 is not satisfied, the high-precision map data generated in step S3 is deleted, the map of the scene is re-adapted, and then steps S1 to S4 are re-executed; and if the detection is satisfied, finishing the construction of the map and publishing the high-precision map.

It should be noted that, with respect to the rapid high-precision map construction method provided by the present invention, an outdoor scene (e.g. square) with good GNSS signals is adapted according to a specific route, and a high-precision map containing information such as a scene passable area, a special function point or a special task area, a reference path, and an operation task is automatically generated according to a high-precision trajectory of vehicle operation and marked special function points or special task function area data in an adaptation process, so as to finally achieve the scene map construction efficiency, low computing resource consumption, and precision requirements, improve the construction efficiency of the high-precision map, and improve the utilization rate and adaptability of unmanned vehicles to GNSS signals, especially the adaptability to environments such as a large square.

In addition, based on the above fast high-precision map construction method provided by the present invention, in order to execute the above fast high-precision map construction method, the present invention also provides a fast high-precision map construction device, which includes the following modules:

the sensor data preprocessing module is used for acquiring original measurement data of a plurality of preset sensors including a GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether a GNSS signal of the outdoor scene is good or not according to the original measurement data of the GNSS (global navigation satellite system), and if so, continuously performing preset format processing operation on preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle;

the vehicle track and function information processing module is connected with the sensor data preprocessing module and is used for generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to preset useful data of a plurality of preset sensors processed by the sensor data preprocessing module;

the high-precision map processing module is connected with the vehicle track and function information processing module and used for processing the running track of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle track and function information processing module to generate a high-precision map and then sending the high-precision map to the high-precision map processing module;

and the map integrity detection module is connected with the high-precision map processing module and is used for carrying out integrity detection on the high-precision map generated by the high-precision map processing module to finally obtain the complete high-precision map.

In addition, the invention also provides a vehicle which comprises the rapid high-precision map constructing device.

Compared with the prior art, the rapid high-precision map construction method, the rapid high-precision map construction device and the vehicle provided by the invention have scientific design, can effectively improve the construction efficiency of the high-precision map, and simultaneously improve the utilization rate and adaptability of the unmanned vehicle to GNSS signals, especially the adaptability of the unmanned vehicle to environments such as large squares and the like, and have great practical significance.

The invention is a map construction method with high efficiency, low consumption and high precision, has scientific and easily realized design principle, clear logic and good scene adaptability to GNSS signals, meets the technical requirements of unmanned vehicles on construction efficiency, calculation resource consumption and precision of high-precision scene maps, is beneficial to ensuring the safety of automatic driving vehicles, quickens the commercialized landing pace of the unmanned vehicles and ensures reliable commercialized landing.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and amendments can be made without departing from the principle of the present invention, and these modifications and amendments should also be considered as the protection scope of the present invention.

Claims (10)

1. A quick high-precision map construction method is characterized by comprising the following steps:

the method comprises the following steps that S1, in an outdoor scene, original measurement data of a plurality of preset sensors including GNSS installed on an unmanned vehicle are obtained, whether GNSS signals of the outdoor scene are good or not is judged according to the original measurement data of the GNSS, and if yes, preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle are continuously processed through preset format processing;

s2, generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to the preset useful data of the plurality of preset sensors processed in the step S1;

step S3, processing the driving track of the unmanned vehicle obtained in the step S2 and the marked special function points or special task function areas to generate a high-precision map;

and S4, carrying out integrity detection on the high-precision map generated in the step S3, and finally obtaining the complete high-precision map.

2. The high-precision map construction method according to claim 1, wherein the step S1 specifically comprises the following sub-steps:

step S11, acquiring original measurement data of a plurality of preset sensors including GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether GNSS signals of the outdoor scene are good or not according to the original measurement data of the GNSS, and if so, continuing to execute the step S12;

the method comprises the steps that a plurality of sensors are preset, wherein the sensors comprise a GNSS, an IMU and a wheel speed meter;

and S12, according to the format requirement of the EKF fusion algorithm of the extended Kalman filter, analyzing the original measurement data in the step S11 into a corresponding specific format, and continuously executing the step S2.

3. The high-precision mapping method according to claim 1, wherein in step S11, the GNSS is configured to provide GNSS signal raw measurement data of the unmanned vehicle, and specifically includes: the longitude and latitude, the course, the speed in the northeast direction, the satellite number, the state, the course flag bit and the horizontal precision factor of the GNSS signal;

the IMU is used for providing IMU raw measurement data of the unmanned vehicle, and specifically comprises the following steps: acceleration and angular velocity in x, y and z directions under the IMU coordinate system;

the wheel speed meter is used for providing wheel speed raw measurement data of the unmanned vehicle, and specifically comprises left wheel speed and right wheel speed of the unmanned vehicle;

in step S12, useful data are preset in the raw measurement data of step S11, specifically including latitude and longitude, heading, speed in the northeast direction in the GNSS signal measured by the GNSS, acceleration and angular speed in the x, y and z directions measured by the inertial measurement unit IMU, and left wheel speed and right wheel speed of the unmanned vehicle measured by the wheel speed meter;

in step S11, when the state of the GNSS signal, the number of stars collected, the horizontal accuracy factor, and the heading flag meet the requirement and reach or exceed the set threshold, it is determined that the GNSS signal of the outdoor scene is good.

4. The high-precision map construction method according to claim 1, wherein the step S2 specifically comprises the following sub-steps:

step S21, calculating the change amount of the pose of the unmanned vehicle in a preset period according to the preset useful data of the inertial measurement unit IMU and the preset useful data of the wheel speed meter processed in the step S1, and calculating the latest predicted pose of the unmanned vehicle after the preset period by combining the unmanned vehicle fusion pose calculated in the last preset period by the Global Navigation Satellite System (GNSS);

step S22, processing the preset useful data of the GNSS processed in the step S1, the latest predicted pose of the unmanned vehicle obtained in the step S21, the pose information calculated and provided by the inertial measurement unit IMU and the left wheel speed and the right wheel speed of the unmanned vehicle measured by the wheel speed meter by an extended Kalman filtering EKF fusion algorithm to obtain high-precision fused pose information of the unmanned vehicle and the running track of the unmanned vehicle;

the fusion pose information comprises fusion position information and fusion attitude information;

and step S23, judging whether the current outdoor scene is matched completely, if not, returning to execute the step S1, and if the scene is matched, continuing to execute the step S3, namely, inputting each item of stored and marked data to the following step S3.

5. The high-precision map building method according to claim 4, further comprising, in step S22, the steps of: marking a special function point or a special task function area according to the requirement of an operation user, and specifically comprising the following processing steps of:

step S220, detecting whether an operator has a marking requirement for inputting a special function point or a special task function area to the fusion position in real time for the fusion position information of the unmanned vehicle subjected to fusion processing, if so, correspondingly marking the special function point or the special task function area at the fusion position, modifying the function attribute information of the special function point or the special task function area, and if not, storing the pose information of the unmanned vehicle subjected to fusion processing in real time.

6. The high-precision map construction method according to claim 1, wherein the step S3 specifically comprises the following sub-steps:

step S31, expanding the driving track of the unmanned vehicle obtained in the step S2 outwards by a preset distance value K to obtain a passable area where the unmanned vehicle drives under the current outdoor scene, and storing the passable area in a specific format;

in step S31, for different outdoor scenes, there are corresponding preset distance values K respectively;

step S32, generating corresponding position and posture information of the special function points or function area boundaries of the special task function area and the passable area boundaries according to the function attribute information of the special function points or the special task function area marked in the step S2;

and S33, according to the existing font-returning strategy or global path planning strategy, automatically generating a reference path of the unmanned vehicle in normal operation according to the corresponding relation between the special function points and the special task function areas, the passable area boundary and the special task function area boundary information, generating all associated operation tasks in the current outdoor scene according to the corresponding relation between the special function points and the special task function areas, and storing the operation tasks to obtain the high-precision map.

7. The high-precision map construction method according to claim 1, wherein the step S4 specifically comprises the following sub-steps:

step S41, detecting whether the file of the high-precision map generated in the step S3 is complete, if so, continuing to execute the step S42, and if not, returning to execute the step S1;

and step S42, detecting whether preset high-precision necessary elements in the high-precision map generated in the step S3 are complete, if so, taking the high-precision map generated in the step S3 as a high-precision map which is finally issued outwards, and if not, returning to execute the step S1.

8. The high accuracy map building method according to claim 7, wherein in step S42, the preset high accuracy necessary elements including a special function point, a special task function area, a reference path, and a task;

detecting whether all preset high-precision necessary elements in the high-precision map generated in the step S3 are complete, specifically: detecting whether three elements, namely a special function point, a special task function area and a reference path, exist or not, and if so, considering the three elements to be complete; and for the task, detecting whether the reference path corresponding to the operation task is communicated, if so, considering the task to be complete, otherwise, considering the task to be incomplete.

9. A quick high-precision map building device is characterized by comprising the following modules:

the sensor data preprocessing module is used for acquiring original measurement data of a plurality of preset sensors including a GNSS (global navigation satellite system) installed on the unmanned vehicle in an outdoor scene, judging whether a GNSS signal of the outdoor scene is good or not according to the original measurement data of the GNSS (global navigation satellite system), and if so, continuously performing preset format processing operation on preset useful data in the original measurement data of the plurality of preset sensors on the unmanned vehicle;

the vehicle track and function information processing module is connected with the sensor data preprocessing module, and is used for generating a running track of the unmanned vehicle and marking a special function point or a special task function area according to preset useful data of a plurality of preset sensors processed by the sensor data preprocessing module;

the high-precision map processing module is connected with the vehicle track and function information processing module and used for processing the driving track of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle track and function information processing module to generate a high-precision map and then sending the high-precision map to the unmanned vehicle;

and the map integrity detection module is connected with the high-precision map processing module and is used for carrying out integrity detection on the high-precision map generated by the high-precision map processing module so as to finally obtain the complete high-precision map.

10. A vehicle comprising the rapid high-precision mapping apparatus of claim 9.

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