A fast and high-precision map construction method, device and vehicle

Abstract

The present invention discloses a fast and high-precision map construction method, including the steps: Step S1, in an outdoor scene, according to the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scene is good. If so, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of a plurality of preset sensors on the unmanned vehicle; Step S2, generate the driving trajectory of the unmanned vehicle and mark special function points or special task function areas according to the preset data to be processed of the sensors processed in Step S1; Step S3, process the driving trajectory of the unmanned vehicle obtained in Step S2 and the marked special function points or special task function areas to generate a high-precision map; Step S4, perform integrity detection on the high-precision map to obtain a complete high-precision map. The present invention can effectively improve the construction efficiency of the high-precision map, and at the same time improve the utilization rate and adaptability of the unmanned vehicle to the GNSS signal.

Description

Technical Field

[0001] The present invention relates to the technical fields of autonomous driving and high-precision map construction, and particularly relates to a fast high-precision map construction method, a device and a vehicle thereof. Background Art

[0002] Autonomous driving technology has been a hot topic in recent years. In the fields of alleviating traffic congestion, improving road safety, reducing air pollution, etc., autonomous driving will bring subversive changes.

[0003] In the commercialization process of autonomous driving, unmanned cleaning vehicles, unmanned express delivery vehicles, and unmanned taxis in limited area scenarios provide substantial application scenarios for the implementation of autonomous driving technology. And high-precision maps can provide prior map information for vehicles. Therefore, the autonomous driving technology based on high-precision maps has become the best solution for realizing L4 and L5 levels of autonomous driving in the industry.

[0004] With the increasing prominence of the demand for autonomous driving in related industries, the landing scenarios of autonomous driving have become clearer. Especially in outdoor scenarios with good GNSS (Global Navigation Satellite System) signals, the demand for vehicles such as unmanned sanitation and unmanned delivery is continuously increasing, which poses a test to the construction efficiency, quality and accuracy of autonomous driving maps.

[0005] Currently, in the autonomous driving industry, the more commonly used map construction method is to construct a point cloud or a raster map based on laser or visual data. This high-precision map construction method is mainly divided into two strategies: offline and online.

[0006] Among them, for the strategy of offline constructing a high-precision map, first, relevant sensor data is collected through vehicle and other acquisition devices, then the offline data is transmitted to the local or the cloud, and finally, the high-precision map is constructed locally or in the cloud. In the strategy of offline constructing a high-precision map, it involves cumbersome processes such as uploading and downloading of raw data, backup and distribution of result data, etc., which requires a lot of human intervention and has a low degree of automation, resulting in reduced efficiency and serious waste of human resources and local computing resources.

[0007] For the strategy of online constructing a high-precision map, first, relevant sensor data of the vehicle is obtained in real time, and a local map is obtained by calculating the sensor data. Secondly, the map is optimized in real time according to the set strategy. Finally, a globally consistent high-precision map is obtained. For the strategy of online constructing a high-precision map, since the construction tasks of the point cloud or raster map are all completed in the vehicle-mounted processor, it has high performance requirements and is difficult to process large maps, and the map quality and error cannot be guaranteed.

[0008] For existing methods of constructing point clouds or raster maps based on laser or visual data, in scenarios with good GNSS signals such as squares, the construction effect of high-precision maps is not friendly. In scenarios with good GNSS signals and simple requirements, the construction process of point clouds or raster maps by this method is complex, time-consuming, and resource-consuming. Summary of the Invention

[0009] The object of the present invention is to provide a fast high-precision map construction method, its device, and vehicle for the technical defects existing in the prior art.

[0010] To this end, the present invention provides a fast high-precision map construction method, which includes the following steps:

[0011] Step S1, in an outdoor scenario, obtain the original measurement data of a preset plurality of sensors installed on the unmanned vehicle, including GNSS. Then, according to the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scenario is good. If so, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset plurality of sensors on the unmanned vehicle.

[0012] Step S2, generate the driving trajectory of the unmanned vehicle and mark special function points or special task function areas according to the preset data to be processed of the preset plurality of sensors processed in Step S1.

[0013] Step S3, process the driving trajectory of the unmanned vehicle and the marked special function points or special task function areas obtained in Step S2 to generate a high-precision map.

[0014] Step S4, perform integrity detection on the high-precision map generated in Step S3 to finally obtain a complete high-precision map.

[0015] Preferably, Step S1 specifically includes the following sub-steps:

[0016] Step S11, in an outdoor scenario, obtain the original measurement data of a preset plurality of sensors installed on the unmanned vehicle, including GNSS. Then, according to the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scenario is good. If so, continue to execute Step S12.

[0017] Among them, the preset plurality of sensors include GNSS, IMU, and wheel speed sensors.

[0018] Step S12, parse the preset data to be processed in the original measurement data of Step S11 into a corresponding specific format according to the format requirements of the extended Kalman filter (EKF) fusion algorithm, and continue to execute Step S2.

[0019] Preferably, in step S11, the GNSS is used to provide the original measurement data of the GNSS signal of the unmanned vehicle, specifically including: the longitude and latitude, heading, velocity in the north-east-down direction, number of satellites received, status, heading flag bit, and horizontal dilution of precision of the GNSS signal;

[0020] The IMU is used to provide the original measurement data of the IMU of the unmanned vehicle, specifically including: the acceleration and angular velocity in the x, y, and z directions in the IMU coordinate system;

[0021] The wheel speed sensor is used to provide the original measurement data of the wheel speed of the unmanned vehicle, specifically including the left wheel speed and the right wheel speed of the unmanned vehicle;

[0022] In step S12, the data to be processed is preset in the original measurement data of step S11, specifically including the longitude and latitude, heading, velocity in the north-east direction of the GNSS signal measured by the GNSS, the acceleration and angular velocity in the x, y, and z directions measured 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 sensor;

[0023] Among them, in step S11, when the status, number of satellites received, horizontal dilution of precision, and heading flag bit of the GNSS signal meet the requirements and reach or exceed the set threshold, it is determined that the GNSS signal in the outdoor scene is good.

[0024] Preferably, step S2 specifically includes the following sub-steps:

[0025] Step S21, according to the preset data to be processed of the inertial measurement unit IMU and the wheel speed sensor processed in step S1, calculate the change amount of the pose of the unmanned vehicle within a preset period, and combine the fused pose of the unmanned vehicle calculated by the global navigation satellite system GNSS in the previous preset period to calculate the latest predicted pose of the unmanned vehicle after the preset period;

[0026] Step S22, process the preset data to be processed of the GNSS processed in step S1, the latest predicted pose of the unmanned vehicle obtained in step S21, the attitude 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 sensor through the extended Kalman filter EKF fusion algorithm to obtain the high-precision fused pose information of the unmanned vehicle after fusion processing and the driving trajectory of the unmanned vehicle;

[0027] Among them, the fused pose information includes fused position information and fused attitude information;

[0028] Step S23: Determine whether the current outdoor scene adaptation is completed. If not, return to step S1. If the scene adaptation is completed, continue to execute step S3. That is, input the stored and marked data into the subsequent step S3.

[0029] Preferably, in step S22, it also includes the step of marking special function points or special task function areas according to the needs of the operating user, specifically including the following processing steps:

[0030] Step S220: For the fused position information of the unmanned vehicle after fusion processing, detect in real time whether the operating user has a marking requirement for inputting special function points or special task function areas at this fused position. If so, mark the special function points or special task function areas at this fused position and modify the function attribute information of the special function points or special task function areas. If not, store the pose information of the unmanned vehicle after fusion processing in real time.

[0031] Preferably, step S3 specifically includes the following sub-steps:

[0032] Step S31: Expand the driving trajectory of the unmanned vehicle obtained in step S2 outward by a preset distance value K to obtain the passable area where the unmanned vehicle travels in the current outdoor scene and store it in a specific format.

[0033] In step S31, for different outdoor scenes, there are corresponding preset distance values K.

[0034] Step S32: Generate corresponding special function point pose information or the function area boundary and passable area boundary of the special task function area according to the function attribute information of the special function points or special task function areas marked in step S2.

[0035] Step S33: According to the existing back-and-forth strategy or global path planning strategy, based on the correspondence between special function points and special task function areas, the passable area boundary, and the special task function area boundary information, automatically generate a reference path for the unmanned vehicle during normal operation, and generate all relevant operation tasks in the current outdoor scene according to the correspondence between special function points and special task function areas, and then store them to obtain a high-precision map.

[0036] Preferably, step S4 specifically includes the following sub-steps:

[0037] Step S41: Detect whether the file of the high-precision map generated in step S3 is complete. If so, continue to execute step S42. If not, return to execute step S1.

[0038] Step S42: Detect whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete. If so, use the high-precision map generated in step S3 as the finally released high-precision map. If not, return to execute step S1.

[0039] Preferably, in step S42, the preset high-precision necessary elements include special function points, special task function areas, reference paths, and tasks.

[0040] Among them, detecting whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete is specifically as follows: For the three elements of special function points, special task function areas, and reference paths, detect whether they exist. If they exist, it is considered that these three elements are complete; for tasks, detect whether the reference path corresponding to the operation task is connected. If it is connected, it is considered that the task is complete, otherwise, it is considered that the task is incomplete.

[0041] In addition, the present invention also provides a fast high-precision map construction device, which includes the following modules:

[0042] The sensor data preprocessing module is used to obtain the original measurement data of a preset plurality of sensors installed on the unmanned vehicle in an outdoor scene, and then, according to the original measurement data of GNSS (i.e., the Global Navigation Satellite System), determine whether the GNSS signal in the outdoor scene is good. If so, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset plurality of sensors on the unmanned vehicle.

[0043] The vehicle trajectory and function information processing module is connected to the sensor data preprocessing module, and generates the driving trajectory of the unmanned vehicle and marks special function points or special task function areas according to the preset data to be processed of the preset plurality of sensors processed by the sensor data preprocessing module.

[0044] The high-precision map processing module is connected to the vehicle trajectory and function information processing module, and is used to process the driving trajectory of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle trajectory and function information processing module, generate a high-precision map, and then send it to;

[0045] The map integrity detection module is connected to the high-precision map processing module, and is used to perform integrity detection on the high-precision map generated by the high-precision map processing module to finally obtain a complete high-precision map.

[0046] In addition, the present invention also provides a vehicle, including the aforementioned fast high-precision map construction device.

[0047] As can be seen from the technical solutions provided by the present invention above, compared with the prior art, the present invention provides a fast and high-precision map construction method, its device, and a vehicle. Its design is scientific, which can effectively improve the construction efficiency of high-precision maps, and at the same time improve the utilization rate and adaptability of unmanned vehicles to GNSS signals, especially the adaptability in environments such as large squares, and has great practical significance.

[0048] For the present invention, it is a map construction method with high efficiency, low consumption, and high precision. Its design principle is scientific, easy to implement, and has clear logic. It has good adaptability to GNSS signals in good scenarios, meeting the technical requirements of unmanned vehicles for the construction efficiency, computational resource consumption, and precision of high-precision scenario maps, which is conducive to ensuring the safety of autonomous driving vehicles and accelerating the commercialization process of unmanned vehicles, and ensuring reliable commercialization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the main flowchart of a fast and high-precision map construction method provided by the present invention;

[0050] Figure 2 It is the overall working flowchart of a fast and high-precision map construction method provided by the present invention;

[0051] Figure 3 It is a schematic diagram of the passable area of an unmanned vehicle in an outdoor scenario obtained by applying the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below with reference to the drawings and embodiments.

[0053] See Figure 1 、 Figure 2 The present invention provides a fast and high-precision map construction method, including the following steps:

[0054] Step S1, in an outdoor scenario, obtain the original measurement data of a preset plurality of sensors installed on the unmanned vehicle, including GNSS. Then, according to the original measurement data of GNSS (i.e., the Global Navigation Satellite System), determine whether the GNSS signal in the outdoor scenario is good. If so, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset plurality of sensors on the unmanned vehicle;

[0055] It should be noted that if the GNSS signal is not good, no further operation will be performed. At this time, an existing high-precision map construction method can be selected.

[0056] In the present invention, step S1 specifically includes the following sub-steps:

[0057] Step S11: In an outdoor scenario, obtain the original measurement data of a preset number of sensors installed on the unmanned vehicle, including GNSS. Then, based on the original measurement data of GNSS (i.e., the Global Navigation Satellite System), determine whether the GNSS signal in the outdoor scenario is good. If it is, continue to execute Step S12; otherwise, do not continue.

[0058] Among them, the preset number of sensors includes GNSS (i.e., the Global Navigation Satellite System), IMU (Inertial Measurement Unit), and wheel speed sensors.

[0059] It should be noted that in scenarios such as squares with good GNSS signals, a good GNSS signal means that the status, number of satellites received, horizontal dilution of precision, and heading flag bit of the GNSS signal meet the requirements and reach or exceed the set threshold.

[0060] In Step S11, when the status, number of satellites received, horizontal dilution of precision, and heading flag bit of the GNSS signal meet the requirements and reach or exceed the set threshold, it is determined that the GNSS signal in the outdoor scenario is good.

[0061] It should be noted that GNSS (i.e., the Global Navigation Satellite System) is a space-based radio navigation and positioning system that can provide users with all-weather three-dimensional coordinates, speed, and time information at any location on the Earth's surface or near-Earth space.

[0062] In the present invention, in Step S11, the Global Navigation Satellite System GNSS is used to provide the original measurement data of the GNSS signal of the unmanned vehicle, specifically including: longitude and latitude, heading, speed in the north-east-down direction, number of satellites received, status, heading flag bit, and horizontal dilution of precision of the GNSS signal.

[0063] It should be noted that the number of satellites received, status, heading flag bit, and horizontal dilution of precision of the GNSS signal are evaluation parameters for determining whether the GNSS signal is good.

[0064] The Inertial Measurement Unit IMU is used to provide the original measurement data of the IMU of the unmanned vehicle, specifically including: accelerations and angular velocities in the x, y, and z directions in the IMU coordinate system.

[0065] The wheel speed sensors are used to provide the original measurement data of the wheel speed of the unmanned vehicle, specifically including the left wheel speed and right wheel speed of the unmanned vehicle.

[0066] Step S12, according to the format requirements of the EKF (Extended Kalman Filter) fusion algorithm, parse the preset data to be processed in the original measurement data of step S11 into corresponding specific formats (such as png, xml, etc.), and then continue to execute step S2.

[0067] In step S12, the preset data to be processed in the original measurement data of step S11 specifically includes the longitude, latitude, heading, and velocity in the north-east local direction in the GNSS signal measured by GNSS, the accelerations and angular velocities in three directions (x, y, z) measured by the inertial measurement unit IMU, and the left and right wheel velocities of the unmanned vehicle measured by the wheel speedometer.

[0068] It should be noted that for the present invention, there is no mention of data storage in S1. After extracting the required useful data from the original measurement data of each sensor, the amount of data obtained is very small, and it is a real-time processing of single-frame sensor data. Therefore, it can be directly stored in the cache. The parsing method is also very simple. Just extract the preset data to be processed required by each sensor and store it in a defined data structure (such as png, xml, etc.), so that it can be input for the next step of operation.

[0069] Step S2, generate the driving trajectory of the unmanned vehicle and mark special function points or special task function areas according to the preset data to be processed of the preset multiple sensors that have undergone step S1 processing;

[0070] In the present invention, step S2 specifically includes the following sub-steps:

[0071] Step S21, according to the preset data to be processed of the inertial measurement unit IMU and the preset data to be processed of the wheel speedometer that have undergone step S1 processing, calculate the change amount of the pose (including position and attitude) of the unmanned vehicle within a preset period (i.e., a period of time with a preset length), and combine the fused pose of the unmanned vehicle calculated by the Global Navigation Satellite System GNSS in the previous preset period (specifically, at a certain moment within this period) to calculate the latest predicted pose (including position and attitude) of the unmanned vehicle after the preset period.

[0072] It should be noted that in step S21, the change amount of the pose of the unmanned vehicle within a preset period (i.e., a period of time with a preset length) specifically includes: the change amounts of the eastward velocity and position, the northward velocity and position, the upward velocity and position, and the change amounts of the roll angle, pitch angle, and yaw angle.

[0073] In step S21, the preset period is related to the vehicle speed, and the preset period is inversely proportional to the vehicle speed. The higher the vehicle speed, the shorter the period. According to the average speed of the passenger car, the recommended preset period is T (T is less than or equal to 0.01 ms).

[0074] In step S21, based on the preset data to be processed of the inertial measurement unit (IMU) and the preset data to be processed of the wheel speed sensor after step S1 processing, the change in the pose of the unmanned vehicle within a preset period (i.e., a period of time of a preset length) is calculated. This calculation and processing operation is common technical knowledge and uses conventional calculation means, which will not be elaborated here. For example, the wheel speed sensor measures speed, and integrating the speed over the period can obtain the change in position; integrating the angular velocity of the IMU gives the change in angle, integrating the acceleration once gives the change in speed, and integrating it twice gives the change in position.

[0075] It should be noted that in step S21, the algorithm of the present invention runs in a strict periodic system, which is the above-mentioned preset period T, and can ensure that the frequency of the fused pose output is fixed. Specifically, the fused pose of the unmanned vehicle calculated by the GNSS in the previous preset period is obtained at a time point (moment) within the previous preset period T, and the fused pose is output at this time point.

[0076] It should be noted that the pose provided by the GNSS at each moment is an absolute pose. The change in the pose of the unmanned vehicle within a preset period (i.e., a period of time of a preset length), plus the fused pose of the unmanned vehicle calculated by the Global Navigation Satellite System (GNSS) in the previous preset period, can obtain the latest predicted pose after the preset period.

[0077] Step S22: Process the preset data to be processed of the Global Navigation Satellite System (GNSS) after step S1 processing, the latest predicted pose of the unmanned vehicle obtained in step S21, the attitude information calculated and provided by the inertial measurement unit (IMU), and the left and right wheel speeds of the unmanned vehicle measured by the wheel speed sensor through the Extended Kalman Filter (EKF) fusion algorithm to obtain the high-precision fused pose information (including position and attitude) of the unmanned vehicle after fusion processing and the driving trajectory of the unmanned vehicle; wherein, the fused pose information includes fused position information and fused attitude information.

[0078] It should be noted that the Extended Kalman Filter (EKF) fusion algorithm is a known algorithm in the art and is a very mature technology at present. Its function is to fuse the data of each input (GNSS, IMU, and wheel speed sensor) to obtain the final high-precision pose of the unmanned vehicle at this moment.

[0079] In the present invention, in step S22, by storing the fused poses of the driverless vehicle at each moment in chronological order, a driving trajectory of the driverless vehicle can be formed.

[0080] In the present invention, in step S22, it also includes the step of marking special function points or special task function areas according to the needs of the operation user. Specifically, it includes the following processing steps:

[0081] Step S220: For the fused position information of the driverless vehicle after fusion processing, it is detected in real time whether the operation user has a marking requirement for inputting special function points or special task function areas at this fused position. If so, special function points or special task function areas are marked correspondingly at this fused position, and the function attribute information (i.e., specific function information) of the special function points or special task function areas is modified. If not, the pose information (including position and attitude) of the driverless vehicle after fusion processing is stored in real time.

[0082] It should be noted that special function points are pose points with special functions (similar to bus stops), such as the departure point, intermediate stop point, and end point of a passenger car. Special function points are selected by the customer through the buttons on the interaction page for the function point attributes and sent down. The algorithm will detect whether such a command is sent down at the end of each cycle. If it exists, the fused pose and function point attributes (i.e., the specific functions possessed or exerted) at this moment are recorded.

[0083] Special task function areas are areas with special functions, such as the speed limit area of a passenger car, the cleaning area of a sanitation vehicle, as well as the pedestrian lane, intersection, uphill and downhill, and special sign areas on the road. The implementation method is the same as that of function points, only the protocol is different.

[0084] Step S23: Determine whether the current outdoor scene adaptation is completed. If not, return to execute step S1. If the scene adaptation is completed, continue to execute step S3, that is, input the stored and marked data into the subsequent step S3.

[0085] In the present invention, in step S23, adaptation refers to the entire process of constructing a high-precision map of the operating environment of the driverless vehicle.

[0086] In step S23, determining that the adaptation is completed: specifying the corresponding protocol, the customer sends down the end adaptation process through the relevant buttons on the interaction page. After the end of each cycle of the algorithm, it will detect whether there is a sent-down end command.

[0087] That is to say, in step S23, it is judged whether the current outdoor scene adaptation is completed. Specifically: when receiving the adaptation end instruction input by the customer (user), it is judged that the outdoor scene adaptation is completed. If the adaptation end instruction input by the customer (user) is not received, it is judged that the outdoor scene adaptation is not completed.

[0088] Step S3: Process the driving trajectory of the unmanned vehicle obtained in step S2 and the marked special function points or special task function areas to generate a high-precision map.

[0089] In the present invention, step S3 specifically includes the following sub-steps:

[0090] Step S31: Expand the driving trajectory of the unmanned vehicle obtained in step S2 outward (for example, in all directions in three-dimensional space) by a preset distance value (i.e., the K value, which is the average distance between the running route of the unmanned vehicle and the scene boundary during the adaptation of the current outdoor scene, and this value is a fixed value for adaptation constraints) to obtain the passable area where the unmanned vehicle travels in the current outdoor scene, and store it in a specific format (such as png, xml, etc.).

[0091] In the present invention, in step S31, the preset distance value K is an empirical value. For example, if the adaptation scene is a standard urban road, the preset value is several lane width values; if it is a square scene, it is several vehicle width values; if it is a park scene, it is half of the road width, etc. This value will select different fixed values according to different scenes (one-to-one correspondence is a kind of constraint). For example, as Figure 3 shown. Figure 3 It is a schematic diagram of the passable area of the unmanned vehicle in an outdoor scene obtained by applying the present invention.

[0092] In step S31, for different outdoor scenes, there are corresponding preset distance values K.

[0093] In step S31, expanding the driving trajectory of the unmanned vehicle obtained in step S2 outward (for example, in all directions in three-dimensional space) by the preset distance value, the specific operation is as follows: Refer to Figure 3 shown. Specifically, taking the fusion position in the driving trajectory as the center, expand a square area with a distance of K in the "cross" shape (i.e., the four directions of directly in front, directly behind, directly to the left, and directly to the right).

[0094] In the present invention, in step S31, the passable area is the area where the vehicle can travel, while the non-passable area is the area where the vehicle cannot travel. The boundary between the two is the high-precision map boundary, which can be understood as an electronic fence. The above-mentioned area that expands outward in a "cross" shape expands all the fusion positions. The area within is the passable area, and the area outside the expansion area is the non-passable area.

[0095] Step S32: According to the functional attribute information of the special function points or special task functional areas marked in step S2 (specifically marked on the fusion pose of the unmanned vehicle), generate the corresponding special function point pose information or the functional area boundary and passable area boundary of the special task functional area.

[0096] It should be noted that the passable area boundary includes the functional area boundary.

[0097] It should be noted that the function point pose information includes the acceleration and angular velocity in three directions (x, y, z), as well as the roll angle, pitch angle, and yaw angle. The role of the function point pose information is to ensure that the unmanned vehicle meets the requirements of the vehicle pose here.

[0098] The functional area boundary: It is a set of curves composed of position points only including three directions (x, y, z). Its role is to regulate the vehicle to perform different operations or tasks in different areas, such as deceleration, speed limit, steering, etc.

[0099] In the present invention, the acquisition method of the functional area boundary of the special task functional area is the same as that of the function points and functional areas, except that the protocols are different. It includes a starting point and an ending point, and the starting point and ending point of the functional area appear in pairs.

[0100] In the present invention, as shown in Figure 3 Expand outward in a "cross" shape (i.e., the four directions of directly in front, directly behind, directly to the left, and directly to the right). The expanded section is between the starting point and the ending point of the functional area, and the generated functional area boundary is simply and smoothly connected to obtain the boundary curve.

[0101] In the present invention, in step S32, it should be noted that only a part of the type of function points are within the functional area of the special task, and such function points need to automatically generate the corresponding tasks according to the formulated task mode rules (providing the global relationship between the starting point and the ending point of the task for the downstream decision-making and planning).

[0102] In the present invention, in step S32, it should be noted that the special function points must be the starting point or the ending point of the special task functional area. This is the corresponding relationship between the special function points and the special task functional area.

[0103] Step S33: According to the existing back-and-forth strategy or global path planning strategy, based on the correspondence between special function points and special task function areas, the boundaries of passable areas, and the boundary information of special task function areas, automatically generate a reference path for the unmanned vehicle during normal operation. And according to the correspondence between special function points and special task function areas (i.e., special function points are the starting or ending points of special task function areas), generate all relevant (corresponding) operation tasks in the current outdoor scene, and then store them to obtain a high-precision map.

[0104] In step S33, it should be noted that the back-and-forth strategy is a method for achieving full coverage cleaning of the cleaning area of a sweeper and automatically generating a full coverage reference path, which is a conventional method for automatically generating a full coverage reference path. Global path planning is for passenger cars, and only needs to pass through this road without performing various special tasks.

[0105] It should be noted that due to vehicle hardware limitations, the vehicle has a minimum turning radius. Therefore, in step S33, the curve radius at the turning of the reference path of the unmanned vehicle during normal operation needs to be greater than the minimum turning radius of the unmanned vehicle.

[0106] It should be noted that in step S33, regarding the reference path of the unmanned vehicle during normal operation, during normal operation, the unmanned vehicle needs to drive along this route.

[0107] In step S33, it should be noted that based on the existing back-and-forth strategy or global path planning strategy (such as the A* algorithm), according to the correspondence between special function points and special task function areas, the boundaries of passable areas, and the boundary information of special task function areas, the reference path of the unmanned vehicle during normal operation can be automatically generated. This is a publicly known technology and will not be elaborated here.

[0108] In step S33, regarding all possible operation tasks in the current outdoor scene, it means that for the reference route generated through the correspondence between special function points and special task function areas, customers can select specified tasks for operation through the interaction interface.

[0109] For the present invention, according to the correspondence between special function points and special task function areas, several related reference paths are generated, and each associated reference path is an operation task, which are combined into all associated operation tasks.

[0110] Step S4: Perform integrity detection on the high-precision map generated in step S3 to finally obtain a complete high-precision map;

[0111] In the present invention, step S4 specifically includes the following sub-steps:

[0112] Step S41: Detect whether the file of the high-precision map generated in step S3 is complete. If it is, continue to execute step S42; if not, return to execute step S1.

[0113] In step S41, detecting whether the file of the high-precision map generated in step S3 is complete specifically means: detecting whether all folders are generated in step S3 (that is, whether there is a problem that some folders are not generated), and whether all result files are generated (that is, whether there is a problem that some result files are not generated). Without this detection step, the integrity of the high-precision cannot be guaranteed, and thus autonomous driving cannot be achieved.

[0114] Step S42: Detect whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete. If so, use the high-precision map generated in step S3 as the finally externally released high-precision map; if not, return to execute step S1.

[0115] In step S42, the preset high-precision necessary elements include special function points, special task function areas, reference paths, and tasks.

[0116] Among them, detecting whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete specifically means: for the three elements of special function points, special task function areas, and reference paths, detect whether they exist. If they exist, it is considered that these three elements are complete; for tasks, it is to detect whether the reference path corresponding to the operation task (for example, the main operation task input by the customer) is connected (that is, not disconnected). If it is connected, it is considered that the task is complete; otherwise, it is considered that the task is incomplete.

[0117] It should be noted that for the present invention, in the two detections of step S41 and step S42, if one is not satisfied, delete the high-precision map data generated in step S3, re-adapt the map for this scenario, and then repeat steps S1 to S4; if both detections are satisfied, end the construction of the map and release the high-precision map.

[0118] It should be noted that for the fast high-precision map construction method provided by the present invention, it adapts to outdoor scenes with good GNSS signals (such as squares) according to a specific route, and based on the high-precision trajectory of the vehicle during the adaptation process and the data of marked special function points or special task function areas, automatically generates a high-precision map including information such as scene passable areas, special function points or special task areas, reference paths, and operation tasks, finally achieving high efficiency in scene map construction, low consumption of computing resources, and meeting accuracy requirements, improving the construction efficiency of high-precision maps, and at the same time improving the utilization rate and adaptability of unmanned vehicles to GNSS signals, especially the adaptability to environments such as large squares.

[0119] In addition, based on the fast high-precision map construction method provided by the present invention above, 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:

[0120] The sensor data preprocessing module is used to obtain the original measurement data of a preset plurality of sensors installed on the unmanned vehicle in an outdoor scene, and then according to the original measurement data of GNSS (i.e., the Global Navigation Satellite System), determine whether the GNSS signal of the outdoor scene is good. If so, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset plurality of sensors on the unmanned vehicle.

[0121] The vehicle trajectory and function information processing module is connected to the sensor data preprocessing module, and generates the driving trajectory of the unmanned vehicle and marks special function points or special task function areas according to the preset data to be processed of the preset plurality of sensors processed by the sensor data preprocessing module.

[0122] The high-precision map processing module is connected to the vehicle trajectory and function information processing module, and is used to process the driving trajectory of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle trajectory and function information processing module to generate a high-precision map, and then send it to;

[0123] The map integrity detection module is connected to the high-precision map processing module, and is used to perform integrity detection on the high-precision map generated by the high-precision map processing module to finally obtain a complete high-precision map.

[0124] In addition, the present invention also provides a vehicle, and the vehicle includes the above-mentioned fast high-precision map construction device.

[0125] In summary, compared with the prior art, a fast high-precision map construction method, its device and vehicle provided by the present invention are scientifically designed, can effectively improve the construction efficiency of high-precision maps, and at the same time improve the utilization rate and adaptability of unmanned vehicles to GNSS signals, especially the adaptability to environments such as large squares, which has great practical significance.

[0126] For the present invention, it is a map construction method with high efficiency, low consumption and high precision, with a scientific design principle, easy to implement, clear logic, good adaptability to GNSS signal-friendly scenarios, meeting the technical requirements of unmanned vehicles for the construction efficiency, computational resource consumption and precision of high-precision scenario maps, being conducive to ensuring the safety of autonomous driving vehicles, as well as accelerating the commercialization process of unmanned vehicles and ensuring reliable commercial implementation.

[0127] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A fast and high-precision map construction method, characterized in that, It includes the following steps: Step S1: In an outdoor scenario, obtain the original measurement data of a preset number of sensors installed on the unmanned vehicle, including GNSS. Then, based on the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scenario is good. If it is, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset number of sensors on the unmanned vehicle; Step S2: Generate the driving trajectory of the unmanned vehicle and mark special function points or special task function areas based on the preset data to be processed of the preset number of sensors processed in Step S1; Step S3: Process the driving trajectory of the unmanned vehicle and the marked special function points or special task function areas obtained in Step S2 to generate a high-precision map; The special function points are pose points including the departure point, intermediate stop point, and end point of a passenger vehicle; Step S4: Perform an integrity check on the high-precision map generated in Step S3 to finally obtain a complete high-precision map; Step S2 specifically includes the following sub-steps: Step S21: Calculate the change in the pose of the unmanned vehicle within a preset period based on the preset data to be processed of the inertial measurement unit (IMU) and the preset data to be processed of the wheel speed sensor processed in Step S1, and combine the pose of the unmanned vehicle calculated by the Global Navigation Satellite System (GNSS) in the previous preset period to calculate the latest predicted pose of the unmanned vehicle after the preset period; Step S22: Process the preset data to be processed of GNSS processed in Step S1, the latest predicted pose of the unmanned vehicle obtained in Step S21, the attitude information calculated and provided by the inertial measurement unit (IMU), and the left and right wheel speeds of the unmanned vehicle measured by the wheel speed sensor through the Extended Kalman Filter (EKF) fusion algorithm to obtain the high-precision fusion pose information of the unmanned vehicle and the driving trajectory of the unmanned vehicle after being fused and processed by the Extended Kalman Filter (EKF) fusion algorithm; Among them, the fusion pose information includes fusion position information and fusion attitude information; Step S23: Determine whether the current outdoor scenario adaptation is completed. If it is not completed, return to execute Step S1. If the scenario adaptation is completed, continue to execute Step S3; Adaptation: It refers to the entire process of constructing a high-precision map of the operating environment of the unmanned vehicle. To determine whether the current outdoor scenario adaptation is completed, specifically: detect whether an external input adaptation end instruction is received; In Step S22, it also includes the step: Mark special function points or special task function areas according to the needs of the operation user, specifically including the following processing steps: Step S220: For the fusion position information of the unmanned vehicle after fusion processing, continuously detect whether the operation user has a marking requirement for inputting special function points or special task function areas at this fusion position. If so, mark the special function points or special task function areas correspondingly at this fusion position and modify the function attribute information of the special function points or special task function areas. If not, continuously store the pose information of the unmanned vehicle after fusion processing; Step S3 specifically includes the following sub-steps: Step S31: Expand the driving trajectory of the driverless vehicle obtained in Step S2 by a preset distance value K to obtain the passable area for the driverless vehicle to drive in the current outdoor scenario, and store it in a specific format. In Step S31, for different outdoor scenarios, there are corresponding preset distance values K respectively. Step S32: Generate the corresponding special function point pose information or the boundary of the special task functional area and the boundary of the passable area according to the functional attribute information of the special function points or special task functional areas marked in Step S2. Step S33: According to the existing back-shaped strategy or global path planning strategy, automatically generate the reference path of the driverless vehicle during normal operation based on the correspondence between special function points and special task functional areas, the boundary of the passable area, and the boundary information of the special task functional area. Then, generate all relevant operation tasks within the current outdoor scenario based on the correspondence between special function points and special task functional areas, and store them to obtain a high-precision map. The correspondence between special function points and special task functional areas is specifically: the special function point is the start or end of the special task functional area.

2. The high-precision map construction method according to claim 1, wherein Step S1 specifically includes the following sub-steps: Step S11: In the outdoor scenario, obtain the original measurement data of a preset number of sensors installed on the driverless vehicle, including GNSS. Then, according to the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scenario is good. If it is, continue to execute Step S12. Among them, the preset number of sensors includes GNSS, IMU, and wheel speed sensors. Step S12: According to the format requirements of the extended Kalman filter (EKF) fusion algorithm, parse the preset data to be processed in the original measurement data of Step S11 into the corresponding specific format, and continue to execute Step S2.

3. The high-precision map construction method according to claim 1, wherein In Step S11, GNSS is used to provide the original measurement data of the GNSS signal of the driverless vehicle, specifically including: longitude and latitude, heading, velocity in the north-east-down direction, number of satellites received, status, heading flag bit, and horizontal dilution of precision of the GNSS signal. IMU is used to provide the original measurement data of the IMU of the driverless vehicle, specifically including: acceleration and angular velocity in the x, y, and z directions in the IMU coordinate system. The wheel speed sensor is used to provide the original measurement data of the wheel speed of the driverless vehicle, specifically including the left wheel speed and the right wheel speed of the driverless vehicle. In Step S12, the preset data to be processed in the original measurement data of Step S11 specifically includes the longitude and latitude, heading, velocity in the north-east direction of the GNSS signal measured by GNSS, acceleration and angular velocity in the x, y, and z directions measured by the inertial measurement unit (IMU), and the left wheel speed and the right wheel speed of the driverless vehicle measured by the wheel speed sensor. Among them, in Step S11, when the status, number of satellites received, horizontal dilution of precision, and heading flag bit of the GNSS signal meet the requirements and reach or exceed the set threshold, it is determined that the GNSS signal in the outdoor scenario is good.

4. The high-precision map construction method according to claim 1, wherein Step S4 specifically includes the following sub-steps: Step S41: Detect whether the file of the high-precision map generated in step S3 is complete. If it is, proceed to step S42; if not, return to step S1 and execute it again. Step S42: Detect whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete. If they are, use the high-precision map generated in step S3 as the finally released high-precision map; if not, return to step S1 and execute it again.

5. The high-precision map construction method according to claim 4, characterized in that, In step S42, the preset high-precision necessary elements include special function points, special task function areas, reference paths, and tasks. Among them, to detect whether all the preset high-precision necessary elements in the high-precision map generated in step S3 are complete, specifically: for the three elements of special function points, special task function areas, and reference paths, detect whether they exist. If they exist, it is considered that these three elements are complete; for tasks, detect whether the reference path corresponding to the operation task is connected. If it is connected, it is considered that the task is complete; otherwise, it is considered that the task is incomplete.

6. A fast and high-precision map construction device, characterized in that, It includes the following modules: The sensor data preprocessing module is used to obtain the original measurement data of a preset number of sensors installed on the unmanned vehicle, including GNSS, in an outdoor scenario. Then, based on the original measurement data of GNSS, determine whether the GNSS signal in the outdoor scenario is good. If it is, continue to perform a preset format processing operation on the preset data to be processed in the original measurement data of the preset number of sensors on the unmanned vehicle. The vehicle trajectory and function information processing module is connected to the sensor data preprocessing module. According to the preset data to be processed of the preset number of sensors processed by the sensor data preprocessing module, generate the driving trajectory of the unmanned vehicle and mark special function points or special task function areas. Special function points are pose points including the departure point, intermediate stop point, and end point of a passenger vehicle. The high-precision map processing module is connected to the vehicle trajectory and function information processing module and is used to process the driving trajectory of the unmanned vehicle and the marked special function points or special task function areas obtained from the vehicle trajectory and function information processing module to generate a high-precision map, and then send it to; The map integrity detection module is connected to the high-precision map processing module and is used to perform integrity detection on the high-precision map generated by the high-precision map processing module to finally obtain a complete high-precision map. The vehicle trajectory and function information processing module is used to perform the following operations: First, perform the operation of obtaining the predicted pose: According to the preset data to be processed of the inertial measurement unit IMU and the preset data to be processed of the wheel speedometer processed by the sensor data preprocessing module, calculate the change in the pose of the unmanned vehicle within a preset period, and combine the pose of the unmanned vehicle calculated by the global navigation satellite system GNSS in the previous preset period to calculate the latest predicted pose of the unmanned vehicle after the preset period. Then, perform the fusion processing operation: The preset data to be processed of GNSS after being processed by the sensor data preprocessing module, the latest predicted pose of the unmanned vehicle obtained from the first operation, the attitude information calculated and provided by the inertial measurement unit IMU, and the left and right wheel speeds of the unmanned vehicle measured by the wheel speedometer are processed via the extended Kalman filter (EKF) fusion algorithm to obtain the high-precision fusion pose information of the unmanned vehicle and the driving trajectory of the unmanned vehicle after being fused by the EKF fusion algorithm; Among them, the fusion pose information includes fusion position information and fusion attitude information; Then, perform the scene adaptation judgment operation: Judge whether the current outdoor scene adaptation is completed. If not, return to run the sensor data preprocessing module. If the scene adaptation is completed, continue to run the high-precision map processing module; Adaptation: Refers to the entire process of constructing a high-precision map of the operating environment of the unmanned vehicle; Judging whether the current outdoor scene adaptation is completed specifically includes: Detecting whether an adaptation end instruction is received from the outside; During the execution of the fusion processing operation, it also includes the operation: According to the requirements of the operation user, mark special function points or special task function areas. The specific content includes: For the fusion position information of the unmanned vehicle after fusion processing, it is detected in real time whether the operation user has a marking requirement for inputting special function points or special task function areas at this fusion position. If so, mark the special function points or special task function areas correspondingly at this fusion position and modify the function attribute information of the special function points or special task function areas. If not, store the pose information of the unmanned vehicle after fusion processing in real time; The high-precision map processing module is used to perform the following operations: First, perform the passable area acquisition operation: By expanding the driving trajectory of the unmanned vehicle obtained in the vehicle trajectory and function information processing module by a preset distance value K, obtain the passable area where the unmanned vehicle travels in the current outdoor scene and store it in a specific format; Among them, for different outdoor scenes, there are corresponding preset distance values K; Then, perform the information processing operation: Generate the corresponding special function point pose information or the function area boundary and passable area boundary of the special task function area according to the function attribute information of the special function points or special task function areas marked by the vehicle trajectory and function information processing module; Then, perform the map acquisition operation: According to the existing back-and-forth strategy or global path planning strategy, automatically generate the reference path of the unmanned vehicle during normal operation based on the corresponding relationship between the special function points and special task function areas, the passable area boundary, and the special task function area boundary information, and generate all related operation tasks in the current outdoor scene according to the corresponding relationship between the special function points and special task function areas, and then store them to obtain a high-precision map; The corresponding relationship between the special function points and special task function areas is specifically: The special function points are the starting or ending points of the special task function areas.

7. A vehicle, characterized in that, Including the fast high-precision map construction device described in claim 6.

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