A mapping method, device, system and storage medium

Abstract

The application relates to the field of map construction, and provides a mapping method, device, system and storage medium. The method comprises the following steps: acquiring a steering angle of a driving wheel of a vehicle, IMU data and distance information of a vehicle body in real time; calculating speed information of the vehicle according to the number of driving wheels of the vehicle, the distance information and the steering angle of the driving wheel; acquiring real-time pose data of the vehicle according to the steering angle of the driving wheel and the speed information; calibrating the real-time pose data according to the IMU data; collecting corresponding point cloud data through a laser radar arranged on the vehicle body; fusing the calibrated real-time pose data and the point cloud data; and completing mapping. According to the application, the number of driving wheels, the IMU data and the point cloud data are combined to calculate real-time pose data of the vehicle in a movement process, mapping is completed, positioning uncertainty caused by only relying on the point cloud data is avoided, and the accuracy of real-time pose calculation and the reliability of mapping are improved.

Description

Technical Field

[0001] This application relates to the field of map construction, and more particularly to a map construction method, apparatus, system and storage medium. Background Technology

[0002] Currently, by using LiDAR to detect and locate the geometric information of the surrounding environment, a geometric map of the entire environment can be constructed. However, in certain special scenarios with few feature points (such as corridors), the illumination intensity information in the point cloud data collected by LiDAR becomes indistinguishable due to the limited number of feature points in the scene. The only variable in the point cloud data is the timestamp information, and relying solely on timestamp information makes accurate positioning difficult, thus hindering the construction of accurate and reliable maps. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the prior art, this application provides a mapping method, apparatus, system and storage medium.

[0004] Firstly, this application provides a mapping method, including:

[0005] The vehicle's motion information is acquired in real time, including the steering angle of the drive wheels, IMU data of the vehicle body, and distance information.

[0006] The vehicle's speed information is calculated based on the number of drive wheels, the distance information, and the steering angle of the drive wheels.

[0007] Based on the steering angle of the drive wheel and the speed information, the real-time pose data of the vehicle is obtained;

[0008] The real-time pose data is calibrated based on the IMU data;

[0009] The vehicle uses a lidar system mounted on its body to collect point cloud data. The calibrated real-time pose data is then fused with the point cloud data to complete the mapping.

[0010] In an optional implementation, the distance information includes the number of pulses output per unit time and the travel distance of the vehicle's drive wheel in a single pulse; calculating the vehicle's speed information based on the number of drive wheels, the distance information, and the steering angle of the drive wheels includes:

[0011] If the vehicle has only one drive wheel, the wheel speed of the drive wheel is obtained based on the number of pulses and the travel distance.

[0012] The speed of the driving wheel is defined as the speed of the center point of the axis of the driving wheel;

[0013] The linear velocity of the vehicle is obtained based on the steering angle of the drive wheel and the velocity of the center point of the drive wheel's axis.

[0014] In an optional implementation, the distance information includes the number of pulses output per unit time and the travel distance of the vehicle's drive wheel in a single pulse; calculating the vehicle's speed information based on the number of drive wheels, the distance information, and the steering angle of the drive wheels includes:

[0015] If the vehicle has at least two drive wheels, then any one drive wheel can be selected from all drive wheels as a specific drive wheel.

[0016] The wheel speed of the specific drive wheel is obtained based on the number of pulses and the travel distance.

[0017] The steering angle of the center point of the axis of the driving wheel is obtained based on the steering angle of all the driving wheels;

[0018] The speed of the center point of the axis of the drive wheel is obtained based on the speed of the specific drive wheel and the steering angle of the center point of the drive wheel's axis.

[0019] The linear velocity of the vehicle is obtained based on the velocity at the center point of the axis and the steering angle at the center point of the drive wheel's axis.

[0020] In an optional implementation, calibrating the real-time pose data based on the IMU data includes:

[0021] Based on the azimuth data at each moment in the IMU data, the first angle increment corresponding to the azimuth of the vehicle per unit time is obtained;

[0022] The azimuth angle in the real-time pose data is calibrated based on the first angle increment.

[0023] In an optional implementation, calibrating the azimuth angle in the real-time pose data based on the first angle increment includes:

[0024] Based on the steering angle of the drive wheel, the second angle increment corresponding to the azimuth angle per unit time is obtained;

[0025] Calculate the difference between the first angle increment and the second angle increment;

[0026] If the difference is greater than the first preset threshold, the second angle increment is calibrated by the first angle increment.

[0027] In an optional implementation, obtaining the real-time pose data of the vehicle based on the steering angle of the drive wheel and the speed information includes:

[0028] Obtain the steering angle of the drive wheel corresponding to the first position of the vehicle, and the speed of the center point of the drive wheel axis in the speed information;

[0029] Based on the steering angle of the drive wheel and the velocity of the center point of the drive wheel's axis, real-time pose data corresponding to each moment between the vehicle's movement from the first position to the second position is obtained; the real-time pose data includes the coordinate information of the position at each moment and the azimuth angle of the vehicle.

[0030] In an optional implementation, fusing the calibrated real-time pose data with the point cloud data includes:

[0031] Within the same time period, the first pose change of the vehicle is obtained based on the point cloud data of the lidar;

[0032] The second pose change of the vehicle is obtained based on the calibrated real-time pose data;

[0033] The first pose change is compared with the second pose change. If the difference between the two exceeds a second preset threshold, the point cloud data of the lidar is calibrated using the calibrated real-time pose data within the time period.

[0034] Secondly, this application provides a mapping apparatus, comprising:

[0035] The first acquisition module is used to acquire the vehicle's motion information in real time, including the steering angle of the drive wheels, IMU data of the vehicle body, and distance information.

[0036] The calculation module is used to calculate the speed information of the vehicle based on the number of drive wheels, the distance information, and the steering angle of the drive wheels;

[0037] The second acquisition module is used to acquire the real-time pose data of the vehicle based on the steering angle of the drive wheel and the speed information.

[0038] A calibration module is used to calibrate the real-time pose data based on the IMU data;

[0039] The mapping module is used to collect corresponding point cloud data through the LiDAR installed on the vehicle body, and fuse the calibrated real-time pose data with the point cloud data to complete the mapping.

[0040] Thirdly, this application provides a mapping system, including a vehicle and a computing platform;

[0041] The vehicle is used to send motion information to the computing platform;

[0042] The computing platform is used to implement the mapping method described above based on the motion information.

[0043] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed, implements the aforementioned mapping method.

[0044] The embodiments of this application have the following beneficial effects:

[0045] This embodiment acquires the steering angle of the vehicle's drive wheels, IMU data of the vehicle body, and distance information in real time; calculates the vehicle's speed information based on the number of drive wheels, distance information, and steering angle of the drive wheels; acquires the vehicle's real-time pose data based on the steering angle and speed information of the drive wheels; calibrates the real-time pose data based on the IMU data; and collects corresponding point cloud data using a LiDAR installed on the vehicle body. The calibrated real-time pose data is then fused with the point cloud data to complete mapping. This embodiment combines the number of drive wheels, IMU data, and point cloud data to calculate the vehicle's real-time pose data during movement, thereby completing mapping using this real-time pose data. This avoids the low accuracy of positioning caused by relying solely on point cloud data to determine real-time pose, and also avoids the inaccuracy of LiDAR positioning caused by environments with few feature points. This allows for accurate mapping even in application scenarios with few feature points, thus improving the accuracy of real-time pose calculation and the reliability of mapping, expanding the application scenarios and scope of this mapping method, and demonstrating good practicality. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0047] Figure 1 A schematic diagram of an application scenario is shown;

[0048] Figure 2 This illustration shows a schematic diagram of the first implementation of the mapping method in this application.

[0049] Figure 3 A schematic diagram of a single steering wheel model in an embodiment of this application is shown;

[0050] Figure 4 A schematic diagram of a four-wheel front-wheel drive model is shown in an embodiment of this application;

[0051] Figure 5 A schematic diagram of a second embodiment of the mapping method in this application is shown;

[0052] Figure 6 A schematic diagram of the vehicle's movement process in an embodiment of this application is shown;

[0053] Figure 7 A schematic diagram of a third embodiment of the mapping method in this application is shown;

[0054] Figure 8 A schematic diagram of the mapping device in an embodiment of this application is shown. Detailed Implementation

[0055] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0056] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0057] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0058] Furthermore, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) will be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and will not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0059] This application embodiment can be applied to vehicles, specifically in scenarios with relatively simple environmental characteristics. The aforementioned vehicles can include cleaning vehicles, AGVs, cars, trucks, motorcycles, buses, lawnmowers, recreational vehicles, amusement park vehicles, construction equipment, trams, and golf carts, etc., and this application embodiment does not impose any particular limitation. To facilitate understanding of this solution, background technology will be introduced first; please refer to [the relevant documentation / reference needed]. Figure 1 The illustration shows a white corridor as the surrounding environment, within which the vehicle is traveling. Furthermore, when the vehicle's lidar scans, it obtains the reflection intensity and three-dimensional data of the scanned area (referring to the relative distance between the scanned area and the vehicle) based on the received echo signals. However, because the white corridor has relatively few feature points—meaning its shape is relatively regular and its color is uniform—the lidar may produce instances where the reflection intensity and three-dimensional data of the scanned area are identical. This further leads to the vehicle's position being unknowable at different points in time, i.e., the vehicle's positional changes over a period of time are unpredictable. Figure 1 In the scenario, at time t1, the scanned area for the vehicle consists of two walls extending forward; and at time t2, the scanned area for the vehicle also consists of two walls extending forward. Therefore, while the vehicle can roughly estimate the distance traveled between t1 and t2 based on its speed, it cannot travel in a perfectly vertical direction; there must be some degree of skew. Consequently, the vehicle's pose change during this time period is unpredictable, making accurate localization and map construction impossible.

[0060] The above problems are not limited to Figure 1 The white corridor example in the text, as well as other scenes with fewer feature points such as tunnels, also exhibit the above-mentioned problems. Figure 1 The examples provided are merely for the purpose of facilitating understanding of the background technology and do not constitute a limitation on the scope of this application.

[0061] Based on this, this application provides a mapping method that fuses the motion information of the active wheel in the mobile device with the point cloud data of the lidar to determine the location information and thus achieve mapping. This avoids the inability to achieve accurate positioning and mapping in scenarios with few environmental factors by relying solely on lidar, thereby improving the accuracy of real-time positioning and the reliability of mapping.

[0062] In this embodiment of the application, a mobile device is described as a vehicle (such as a cleaning vehicle). The vehicle is equipped with corresponding sensors such as lidar, odometer, angle sensor, and inertial measurement unit (IMU) as well as an embedded processor. Each sensor is used to collect corresponding data during the vehicle's movement (i.e., the cleaning process), such as speed and position data, while the processor is used to process the corresponding data collected by each sensor.

[0063] In one implementation, the mapping method provided in this embodiment can be implemented based on the vehicle itself, that is, the mapping is implemented by executing the corresponding steps based on the vehicle's processor.

[0064] In another implementation, the mapping method can also be implemented using a mapping system, which includes a vehicle and a computing platform. The vehicle can process data collected by various sensors through a processor and then transmit it to the computing platform via a corresponding communication interface. For example, a lidar can collect relevant data (such as distance data) during the vehicle's movement at a fixed frequency and transmit it to the computing platform via a communication interface. The computing platform then analyzes the data and generates point cloud data. The computing platform then executes the steps of the mapping method and, in conjunction with appropriate mapping software or programs (such as Cartographer software), completes the mapping. In this implementation, the computing platform undertakes the complex work of data computation and processing, thereby reducing the data processing pressure on the vehicle's internal processor and improving mapping efficiency.

[0065] Please refer to Figure 2 The following section will take a mapping system that includes vehicles and a computing platform as an example to explain the mapping method in detail.

[0066] S10 acquires vehicle motion information in real time.

[0067] S20 calculates the vehicle's speed information based on the number of drive wheels, distance information, and steering angle of the drive wheels.

[0068] In this embodiment, the vehicle's motion information is acquired in real time by corresponding sensors installed on the vehicle body. The motion information includes the steering angle of the drive wheel, the vehicle body's IMU data, and distance information, while the distance information includes the number of pulses output by the vehicle per unit time and the travel distance corresponding to the vehicle's drive wheel outputting a single pulse.

[0069] Then, the vehicle's speed information is calculated based on this motion information. Each vehicle's drive mode corresponds to a different number of driving and driven wheels, and vehicle types include, for example, three-wheel drive, four-wheel drive, and six-wheel drive vehicles. In this embodiment, the corresponding speed information is calculated by combining the number of driving wheels corresponding to the vehicle.

[0070] Specifically, the vehicle's drive wheel speed is calculated based on the number of pulses output by the vehicle and the travel distance corresponding to a single pulse collected by the odometer. The formula for calculating the vehicle's travel distance is as follows:

[0071] distance = N * P;

[0072] Where N is the number of pulses output per unit time, and P is the distance traveled by the vehicle's driving wheel for each pulse output.

[0073] If the unit time is dt, then the speed of the vehicle's driving wheel (V) W The calculation formula for ) is as follows:

[0074] V W = distance / dt.

[0075] Furthermore, based on the steering angle of the driving wheel collected by the angle sensor and the wheel speed of the driving wheel calculated in the above steps, and combined with the number of driving wheels corresponding to the vehicle, the linear velocity of the vehicle and the velocity of the center point of the axis of the driving wheel are calculated.

[0076] In one embodiment, if there is only one drive wheel, meaning the vehicle is a single-steering wheel driven vehicle, when calculating the vehicle's speed information, specifically, the drive wheel speed (V) is determined based on the number of pulses output per unit time and the distance traveled. W The speed of the driving wheel is defined as the speed at the center point of its axis (V); based on the steering angle of the driving wheel and the speed at the center point of its axis, the linear speed of the vehicle (V0) is calculated. AGV ).

[0077] For example, such as Figure 3 As shown below, the speed information calculation process for a vehicle with only one drive wheel, taking a three-wheel drive vehicle as an example, will be explained. Specifically, in... Figure 3 In the diagram, A is the rotation center of the vehicle's driving wheel, responsible for steering and driving; B and C are the rolling centers of the vehicle's driven wheels; O is the geometric center of the vehicle's driven wheel axis; l is the vehicle's wheelbase; θ is the steering angle of the vehicle's driving wheel center point; and r is the distance from the vehicle's geometric center O to point P, i.e., the transient turning radius during the vehicle's movement.

[0078] Therefore, the specific calculation formula for the speed information of this three-wheel drive vehicle is as follows:

[0079] V = V W ;

[0080] V AGV =V*cosθ.

[0081] In one embodiment, if there are at least two drive wheels, when calculating the vehicle's speed information, firstly, any one drive wheel is selected as a specific drive wheel from all the drive wheels; the wheel speed of the specific drive wheel is calculated based on the number of pulses output by the vehicle per unit time and the corresponding travel distance; then, the steering angle of the drive wheel's axis center point is obtained based on the steering angles of all the drive wheels; specifically, the average value of the steering angles of all the drive wheels is defined as the steering angle of the drive wheel's axis center point.

[0082] Furthermore, based on the speed of a specific driving wheel and the steering angle of the driving wheel's axis center point, the speed of the vehicle's driving wheel's axis center point (i.e., V) is calculated; based on the axis center point speed and the steering angle of the driving wheel's axis center point, the vehicle's linear velocity (i.e., V0) is obtained. AGV ).

[0083] The following explanation uses a four-wheel front-wheel drive vehicle as an example to illustrate the speed information calculation process for vehicles with at least two drive wheels.

[0084] For example, such as Figure 4 As shown, if the vehicle is a four-wheel front-wheel drive vehicle (i.e., the driving wheels are the left front wheel and the right front wheel).

[0085] It should be noted that, in Figure 4 In the diagram, A and B are the rotation centers of the vehicle's left and right drive wheels, responsible for steering and driving, respectively; C and D are the rolling centers of the vehicle's driven wheels; O is the geometric center of the vehicle's driven wheel axis; l is the vehicle's wheelbase; θ L θr is the steering angle of the left driving wheel of the vehicle, and θr is the steering angle of the right driving wheel of the vehicle; r is the distance from the geometric center O of the vehicle to point P, that is, the instantaneous turning radius of the vehicle during its movement.

[0086] Furthermore, if a specific driving wheel is designated as the left driving wheel, then based on the steering angle of all driving wheels (i.e., θ) L Using θr, the steering angle (θ) at the center point of the drive wheel's axis is calculated. The specific calculation process is as follows:

[0087] θ=(θ L +θr) / 2.

[0088] Then, based on the speed of a specific driving wheel and the steering angle of the driving wheel's center point, the speed of the driving wheel's center point (V) and the vehicle's linear velocity (V0) are calculated accordingly. AGV The corresponding calculation formula is as follows:

[0089] V = (V W *sinθ L ) / sinθ;

[0090] V AGV =V*cosθ.

[0091] S30 obtains real-time vehicle posture data based on the steering angle and speed information of the drive wheels.

[0092] Based on the steering angle and speed information of the drive wheels during the vehicle's movement, the real-time pose data of the vehicle during the movement can be calculated, thus determining the changes in the vehicle's pose during the movement.

[0093] In one implementation, such as Figure 5 As shown, step S30 specifically includes the following steps:

[0094] S31, obtain the speed of the center point of the drive wheel axis from the steering angle and speed information of the drive wheel corresponding to the first position of the vehicle.

[0095] S32 obtains real-time pose data of the vehicle at various moments between the first position and the second position, based on the steering angle of the active wheel and the velocity of the center point of the active wheel axis.

[0096] In this embodiment, the real-time pose data includes the position coordinates (i.e., x, y) and the azimuth angle (i.e., α) of the vehicle body at each time point.

[0097] like Figure 6 As shown, if a vehicle moves from the first position (point M) to the second position (point N) within a preset period (Δt), during this process, α is defined as the angle between the vehicle's center and the positive direction of the horizontal coordinate axis (x-axis) in the coordinate system, which is the vehicle's azimuth angle; where the counterclockwise direction is defined as the positive direction, and the original pose of point M is assumed to be (X... M Y M α M Then, the pose of point N (X) is obtained as follows. N Y N α N The calculation process (i.e., the pose calculation formula) is as follows:

[0098]

[0099] Among them, Figure 6 In this context, ω represents the angular velocity of the vehicle, r represents the transient turning radius of the vehicle during its motion, and V represents the velocity of the center point of the axis of the vehicle's driving wheel.

[0100] Furthermore, the calculation process for the transient radius of gyration (r) and angular velocity (ω) is as follows:

[0101]

[0102]

[0103] Combining the above calculation formulas, we can obtain the corresponding pose data relationship between the first and second positions:

[0104]

[0105] It is understandable that when the initial pose data of the vehicle is set or collected, the real-time pose data corresponding to each moment during the movement can be calculated according to the above calculation formula.

[0106] In other words, assuming the vehicle starts moving from the initial position, taking this point as the origin of the world coordinate system, and setting the vehicle's pose data corresponding to the initial position as (0, 0, 0), and using a unit time Δt as a calculation cycle, the real-time pose data of the vehicle at each moment during its movement can be calculated using the above calculation formula.

[0107] S40 calibrates the real-time pose data based on IMU data.

[0108] It should be noted that the azimuth angle of a vehicle during motion specifically refers to the angle (yaw) of the vehicle body rotation around the Z-axis in a three-dimensional coordinate system. Since the rotation angle (i.e., azimuth angle) of the vehicle body is obtained by detecting the wheel rotation angle by an angle sensor, calculating the corresponding number of drive wheels, and continuously integrating, it is prone to cumulative errors. Therefore, in this embodiment, IMU data is used to calibrate the azimuth angle to obtain accurate pose data.

[0109] In one implementation, such as Figure 7 As shown, step S40 specifically includes the following steps:

[0110] S41, based on the azimuth data at each moment in the IMU data, calculate the first angle increment corresponding to the azimuth angle of the vehicle per unit time.

[0111] S42, calibrate the azimuth angle in the real-time pose data according to the first angle increment.

[0112] The first angle increment (Δyaw) corresponding to the azimuth angle per unit time (i.e., within a preset period) is calculated using IMU data collected by the vehicle's inertial measurement unit. In other words, the first angle increment corresponding to the azimuth angle at each moment within a unit time is calculated from the IMU data.

[0113] Based on the steering angle of the driving wheels collected by the vehicle's angle sensor, the second angle increment corresponding to the azimuth angle per unit time is calculated. That is, using the steering angle of the driving wheels and the angular velocity at each moment calculated in the above formula, the second angle increment (Δα) corresponding to the azimuth angle per unit time is calculated. The formula for calculating the second angle increment is as follows:

[0114] Δα=ωΔt.

[0115] Further, the difference between the first angle increment and the second angle increment is calculated; if the difference is greater than a first preset threshold, the second angle increment is calibrated using the first angle increment; otherwise, if the difference is less than or equal to the first preset threshold, no processing is performed. The first preset threshold can be set according to actual conditions and is not limited here.

[0116] The S50 uses a lidar sensor mounted on the vehicle to collect point cloud data, and then fuses the calibrated real-time pose data with the point cloud data to complete the mapping.

[0117] While the vehicle is in motion, the lidar on its body simultaneously collects point cloud data at corresponding moments, and this point cloud data provides feedback on the vehicle's pose at each position at each moment during its motion.

[0118] Furthermore, by fusing the calibrated real-time pose data and point cloud data, the actual pose data of the vehicle at various moments during its movement can be obtained, thereby enabling mapping.

[0119] As an optional implementation, to ensure the accuracy of the actual pose data and the reliability of the mapping, this embodiment calibrates the point cloud data by comparing the point cloud data acquired within the same time period with the calibrated real-time pose data during the fusion process of real-time pose data and point cloud data. That is, by generating data from different data sources using LiDAR and IMU within the same time period to obtain pose changes corresponding to different data sources, the pose changes from different data sources are compared to determine whether calibration is necessary.

[0120] It should be noted that the aforementioned time period should be set according to actual needs, and is not limited here. For example, this time period could be the time period corresponding to two adjacent frames of point cloud data output by the LiDAR.

[0121] It can be understood that within the same time period, the point cloud data acquired by LiDAR can correspond to the vehicle's first pose change; the calibrated real-time pose data corresponding to this time period can be used to obtain the vehicle's second pose change. Specifically, the first pose change is the amount of pose change in the point cloud data within this time period, that is, the variable values ​​of each component (x, y, α) in the point cloud data corresponding to this time period; similarly, the second pose change is the variable values ​​of each component in the real-time pose data corresponding to this time period.

[0122] Furthermore, the first pose change is compared with the second pose change. If the difference between the two exceeds a second preset threshold, the point cloud data of the LiDAR is calibrated using the calibrated real-time pose data within that time period. Conversely, if the difference between the two is less than or equal to the second preset threshold, no processing is performed. The second preset threshold can be set according to actual conditions and is not limited here.

[0123] This embodiment combines the number of drive wheels, IMU data, and point cloud data to calculate the vehicle's pose data at various moments during its movement. Mapping is then performed based on this pose data, avoiding the low accuracy of positioning caused by relying solely on point cloud data collected by LiDAR to determine real-time pose. This also avoids the inaccuracies caused by LiDAR's susceptibility to environments with few feature points. Therefore, this embodiment is applicable to mapping scenarios with fewer feature points, improving the accuracy of real-time pose calculation and the reliability of mapping, expanding the application scenarios and scope of this mapping method, and demonstrating good practicality. Furthermore, this embodiment also provides a specific calculation process for velocity information corresponding to different numbers of drive wheels. Therefore, the mapping method provided in this embodiment can be applied to various navigation mobile devices, expanding the application scenarios and scope of application, and demonstrating considerable practicality.

[0124] Please refer to Figure 8 This application also provides a mapping apparatus, which includes:

[0125] The first acquisition module 71 is used to acquire the vehicle's motion information in real time, including the steering angle of the drive wheels, IMU data of the vehicle body, and distance information.

[0126] The calculation module 72 is used to calculate the speed information of the vehicle based on the number of drive wheels, the distance information, and the steering angle of the drive wheels;

[0127] The second acquisition module 73 is used to acquire the real-time pose data of the vehicle based on the steering angle of the drive wheel and the speed information.

[0128] The calibration module 74 is used to calibrate the real-time pose data based on the IMU data;

[0129] The mapping module 75 is used to collect corresponding point cloud data through the lidar installed on the vehicle body, and fuse the calibrated real-time pose data with the point cloud data to complete the mapping.

[0130] The mapping apparatus described above corresponds to the mapping method of Embodiment 1. Any of the options in Embodiment 1 are also applicable to this embodiment, and will not be described in detail here.

[0131] Optionally, embodiments of this application also provide a mapping system, which includes a vehicle and a computing platform; wherein the vehicle is used to send motion information to the computing platform; and the computing platform is used to implement the various steps of the mapping method as described in the foregoing embodiments based on the motion information.

[0132] The mapping system described above corresponds to the mapping method in Embodiment 1. Any of the options in Embodiment 1 are also applicable to this embodiment, and will not be described in detail here.

[0133] This application also provides a computer storage medium storing computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the mapping method described in the above embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0135] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0136] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A mapping method, characterized in that, include: The vehicle's motion information is acquired in real time, including the steering angle of the drive wheels, IMU data of the vehicle body, and distance information. The vehicle's speed information is calculated based on the number of drive wheels, the distance information, and the steering angle of the drive wheels. Based on the steering angle of the drive wheel and the speed information, the real-time pose data of the vehicle is obtained; The real-time pose data is calibrated based on the IMU data; The corresponding point cloud data is collected by the lidar installed on the vehicle body, and the calibrated real-time pose data is fused with the point cloud data to complete the mapping. The distance information includes the number of pulses output per unit time and the distance traveled by the vehicle's drive wheel in a single pulse; calculating the vehicle's speed information based on the number of drive wheels, the distance information, and the steering angle of the drive wheels includes: If the vehicle has only one drive wheel, the wheel speed of the drive wheel is obtained based on the number of pulses and the travel distance; the wheel speed of the drive wheel is defined as the speed of the center point of the drive wheel's axis. The linear velocity of the vehicle is obtained based on the steering angle of the drive wheel and the velocity of the center point of the drive wheel's axis. If the vehicle has at least two drive wheels, then any one drive wheel is selected as a specific drive wheel from all the drive wheels; the wheel speed of the specific drive wheel is obtained based on the number of pulses and the travel distance; the steering angle of the drive wheel's axis center point is obtained based on the steering angles of all the drive wheels; the speed of the drive wheel's axis center point is obtained based on the speed of the specific drive wheel and the steering angle of the drive wheel's axis center point; and the linear velocity of the vehicle is obtained based on the speed of the axis center point and the steering angle of the drive wheel's axis center point.

2. The mapping method according to claim 1, characterized in that, The calibration of the real-time pose data based on the IMU data includes: Based on the azimuth data at each moment in the IMU data, the first angle increment corresponding to the azimuth of the vehicle per unit time is obtained; The azimuth angle in the real-time pose data is calibrated based on the first angle increment.

3. The mapping method according to claim 2, characterized in that, The step of calibrating the azimuth angle in the real-time pose data according to the first angle increment includes: Based on the steering angle of the drive wheel, the second angle increment corresponding to the azimuth angle per unit time is obtained; Calculate the difference between the first angle increment and the second angle increment; If the difference is greater than the first preset threshold, the second angle increment is calibrated by the first angle increment.

4. The mapping method according to claim 1 or 3, characterized in that, The step of obtaining the real-time pose data of the vehicle based on the steering angle of the drive wheel and the speed information includes: Obtain the steering angle of the drive wheel corresponding to the first position of the vehicle, and the speed of the center point of the drive wheel axis in the speed information; Based on the steering angle of the drive wheel and the velocity of the center point of the drive wheel's axis, real-time pose data corresponding to each moment between the vehicle's movement from the first position to the second position is obtained; the real-time pose data includes the coordinate information of the position at each moment and the azimuth angle of the vehicle.

5. The mapping method according to claim 1 or 3, characterized in that, The process of fusing the calibrated real-time pose data with the point cloud data includes: Within the same time period, the first pose change of the vehicle is obtained based on the point cloud data of the lidar; The second pose change of the vehicle is obtained based on the calibrated real-time pose data; The first pose change is compared with the second pose change. If the difference between the two exceeds a second preset threshold, the point cloud data of the lidar is calibrated using the calibrated real-time pose data within the time period.

6. A mapping device, characterized in that, include: The first acquisition module is used to acquire the vehicle's motion information in real time, including the steering angle of the drive wheels, IMU data of the vehicle body, and distance information. The calculation module is used to calculate the speed information of the vehicle based on the number of drive wheels, the distance information, and the steering angle of the drive wheels; The second acquisition module is used to acquire the real-time pose data of the vehicle based on the steering angle of the drive wheel and the speed information. A calibration module is used to calibrate the real-time pose data based on the IMU data; The mapping module is used to collect corresponding point cloud data through the lidar installed on the vehicle body, and fuse the calibrated real-time pose data with the point cloud data to complete the mapping. The distance information includes the number of pulses output per unit time and the distance traveled by the vehicle's drive wheel in a single pulse; the calculation module is specifically used for: If the vehicle has only one drive wheel, the wheel speed of the drive wheel is obtained based on the number of pulses and the travel distance; the wheel speed of the drive wheel is defined as the speed of the center point of the drive wheel's axis. The linear velocity of the vehicle is obtained based on the steering angle of the drive wheel and the velocity of the center point of the drive wheel's axis. If the vehicle has at least two drive wheels, then any one drive wheel is selected as a specific drive wheel from all the drive wheels; the wheel speed of the specific drive wheel is obtained based on the number of pulses and the travel distance; the steering angle of the drive wheel's axis center point is obtained based on the steering angles of all the drive wheels; the speed of the drive wheel's axis center point is obtained based on the speed of the specific drive wheel and the steering angle of the drive wheel's axis center point; and the linear velocity of the vehicle is obtained based on the speed of the axis center point and the steering angle of the drive wheel's axis center point.

7. A mapping system, characterized in that, Including vehicles and computing platforms; The vehicle is used to send motion information to the computing platform; The computing platform is used to implement the mapping method as described in any one of claims 1-5 based on the motion information.

8. A computer storage medium, characterized in that, It stores a computer program, which, when executed, implements the mapping method according to any one of claims 1-5.

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