Odometer state determination method and device, equipment and medium

Abstract

The invention discloses a speedometer state determination method and device, equipment and a medium, and relates to the technical field of speedometers. Comprising the steps of obtaining point cloud data in a target time period, IMU measurement data in the target time period, a wheel speed meter measurement wheel speed in the target time period, laser radar measurement data at a first moment and laser radar measurement data at a second moment; determining a wheel speed error corresponding to the target time period according to the laser radar measured wheel speed at the first moment, the laser radar measured attitude at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the laser radar measured wheel speed at the second moment, the laser radar measured attitude at the second moment and the wheel speed measured by the wheel speed meter at the second moment; and according to the wheel speed error, the IMU measurement data in the target time period, the target point cloud data, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment, solving the state of the speedometer to obtain a speedometer state result. According to the invention, the accuracy of the speedometer state is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of odometers, and in particular to a method, device, equipment and medium for determining an odometer state. Background Art

[0002] Lidar sensor is one of the important sensors in high-level autonomous driving systems and is used in perception, high-precision positioning and mapping modules. Among them, the odometer technology based on Lidar sensors, that is, the technology of processing the point cloud data collected by the sensor to achieve the relative position (position + posture) settlement of the sensor corresponding to two frames of data in the common view area, is one of the most basic and important technologies for high-precision positioning and mapping modules of high-level autonomous driving systems. In the engineering field, the determination of odometer status based on fusion is one of the widely used implementation methods. Among them, the inertial measurement unit (IMU) is a common sensor for determining the fusion odometer status due to its high frequency and short-term stability of its own inertial data.

[0003] In the submodules of high-level autonomous driving systems, especially the application of high-precision positioning modules, accurate estimation of odometer status is a prerequisite for autonomous driving systems. In the prior art, the method for determining the odometer status of the fusion of LiDAR and inertial measurement unit can estimate the odometer status through acceleration integral and position constraints, but the accuracy of the odometer status estimated by the prior art is low. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for determining an odometer state, so as to improve the accuracy of the odometer state.

[0005] In a first aspect, the present invention provides a method for determining an odometer state, comprising:

[0006] Obtain map point data, point cloud data within the target time period, IMU (Inertial Measurement Unit) measurement data within the target time period, wheel speed measured by a wheel speed meter within the target time period, lidar measurement data at the first moment, and lidar measurement data at the second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement attitude;

[0007] Determine the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the laser radar measurement posture at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the laser radar measurement posture at the second moment, and the wheel speed measured by the wheel speed meter at the second moment;

[0008] According to the wheel speed error, IMU measurement data within the target time period, wheel speed measured by the tachometer within the target time period, target point cloud data, map point data, lidar measurement data at the first moment and lidar measurement data at the second moment, the odometer state is solved to obtain the odometer state result corresponding to the target time period.

[0009] In a second aspect, the present invention further provides a device for determining an odometer state, comprising:

[0010] A data acquisition module is used to acquire map point data, point cloud data within a target time period, IMU (Inertial Measurement Unit) measurement data within a target time period, wheel speed measured by a wheel speed meter within a target time period, lidar measurement data at a first moment, and lidar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement attitude;

[0011] an error determination module, for determining a wheel speed error corresponding to a target time period according to the wheel speed measured by the laser radar at a first moment, the attitude measured by the laser radar at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at a second moment, the attitude measured by the laser radar at the second moment, and the wheel speed measured by the wheel speed meter at the second moment;

[0012] The odometer solution module is used to solve the odometer state according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the wheel speed meter within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment, and obtain the odometer state result corresponding to the target time period.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, including:

[0014] at least one processor; and

[0015] a memory communicatively coupled to at least one processor; wherein

[0016] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the method for determining the odometer state provided by any embodiment of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for determining an odometer state according to any embodiment of the present invention when executing the computer instructions.

[0018] The technical solution of the embodiment of the present invention measures the wheel speed by using a laser radar to measure the wheel speed, the laser radar to measure the posture and the wheel speed by using a speedometer to measure the wheel speed, and then solves the odometer state based on the wheel speed error, the IMU measurement data within the target time period, the target point cloud data, the map point data, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment. When solving the odometer state, the wheel speed error can be constrained, thereby improving the accuracy of the wheel speed and other parameters in the odometer state.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a method for determining an odometer state according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a method for determining an odometer state according to a second embodiment of the present invention;

[0023] Figure 3A is a flow chart of a method for determining an odometer state according to Embodiment 3 of the present invention;

[0024] Figure 3B is a schematic diagram of the distance between a point and a plane provided according to Embodiment 3 of the present invention;

[0025] Figure 3C is an operation flow chart of an odometer system provided according to Embodiment 3 of the present invention;

[0026] Figure 4 is a structural schematic diagram of an odometer state determination device provided according to a fourth embodiment of the present invention;

[0027] Figure 5 It is a structural schematic diagram of an electronic device for implementing the method for determining the odometer state of an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first" and "second" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of the target point cloud data and IMU measurement data involved all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a method for determining an odometer state is provided in accordance with a first embodiment of the present invention. This embodiment is applicable to situations where an odometer state is to be solved. The method may be performed by an odometer state determination device, which may be implemented in the form of hardware and / or software and may be specifically configured in an electronic device, such as a server or a vehicle.

[0033] See also Figure 1 The method for determining the odometer state shown includes:

[0034] S101, obtaining map point data, point cloud data within a target time period, IMU measurement data within a target time period, wheel speed measured by a tachometer within a target time period, lidar measurement data at a first moment, and lidar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement posture.

[0035] In this embodiment, the map point data may include the location data of at least one map point in the map coordinate system. The target time period may be a time period for collecting point cloud data; wherein the point cloud data may be real-time point cloud data or point cloud data of a certain frame. It should be noted that the point cloud data of a certain frame is not collected instantaneously, but collected after a certain period of time; the time period formed by the collection start time and the collection end time of the point cloud data of the frame is the target time period corresponding to the point cloud data of the frame. The first moment is the start time of the target time period; the second moment is the end time of the target time period, that is, the first moment is the start time of the point cloud data collection, and the second moment is the end time of the point cloud data collection.

[0036] IMU measurement data may be data measured by the IMU unit, and may include but are not limited to attitude, acceleration, angular velocity, acceleration bias, angular velocity bias, angular velocity noise, acceleration noise, and measurement frequency measured by the IMU unit. The wheel speed measured by the speedometer may be the wheel speed measured by the speedometer. Lidar measurement data may include but are not limited to the position measured by the Lidar, the wheel speed measured by the Lidar, and the attitude measured by the Lidar. Among them, the position measured by the Lidar may be the position of the carrier measured by the Lidar; the wheel speed measured by the Lidar may be the wheel speed of the carrier measured by the Lidar; and the attitude measured by the Lidar may be the attitude of the carrier measured by the Lidar. It should be noted that the present invention does not limit the type of carrier. For example, the carrier may be a vehicle.

[0037] In a specific embodiment, the laser radar, the wheel speed meter and the IMU unit are all located in the same carrier. The IMU measurement data, the wheel speed measured by the wheel speed meter and the laser radar measurement data can be represented in the form of a three-dimensional matrix; the IMU measurement data, the wheel speed measured by the wheel speed meter and the laser radar measurement data all include vertical component data, forward component data and right component data. The laser radar measurement posture can be represented in the form of a rotation matrix. The laser radar, the IMU unit and the wheel speed meter have all been pre-clocked and calibrated for external parameters.

[0038] S102, determine the wheel speed error corresponding to the target time period based on the wheel speed measured by the laser radar at the first moment, the posture measured by the laser radar at the first moment, the wheel speed measured by the speedometer at the first moment, the wheel speed measured by the laser radar at the second moment, the posture measured by the laser radar at the second moment, and the wheel speed measured by the speedometer at the second moment.

[0039] In this embodiment, the wheel speed error may be the error between the wheel speed measured by the laser radar and the wheel speed measured by the wheel speed meter in the target time period. Specifically, a certain algorithm is used to determine the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the laser radar measurement posture at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the laser radar measurement posture at the second moment, and the wheel speed measured by the wheel speed meter at the second moment.

[0040] S103, solve the odometer state according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the speedometer within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment, and obtain the odometer state result corresponding to the target time period.

[0041] In this embodiment, the odometer state may include but is not limited to the laser radar measurement position corresponding to the target time period, the laser radar measurement wheel speed and laser radar measurement attitude, as well as the angular velocity zero bias measured by the IMU unit and the acceleration zero bias measured by the IMU, etc. The odometer state result is the result obtained by solving the odometer state.

[0042] Exemplarily, the odometer state can be expressed by the following formula:

[0043]

[0044] Where, χ represents the odometer state; t represents the position measured by the laser radar; v represents the wheel speed measured by the laser radar; q represents the attitude measured by the laser radar; b ω Indicates the angular velocity zero bias measured by the IMU unit; b a Represents the acceleration zero bias measured by IMU; T represents the transpose of the matrix; Represents a 15-dimensional matrix.

[0045] Specifically, a certain algorithm is adopted to solve the odometer state according to the wheel speed error, IMU measurement data within the target time period, wheel speed measured by the speedometer within the target time period, target point cloud data, map point data, lidar measurement data at the first moment and lidar measurement data at the second moment, so as to obtain the odometer state result corresponding to the target time period.

[0046] The technical solution of the embodiment of the present invention measures the wheel speed by using a laser radar to measure the wheel speed, the laser radar to measure the posture and the wheel speed by using a speedometer to measure the wheel speed, and then solves the odometer state based on the wheel speed error, the IMU measurement data within the target time period, the target point cloud data, the map point data, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment. When solving the odometer state, the wheel speed error can be constrained, thereby improving the accuracy of the wheel speed and other parameters in the odometer state.

[0047] Embodiment 2

[0048] Figure 2 This is a flow chart of a method for determining an odometer state provided in a second embodiment of the present invention. Based on the technical solutions of the above embodiments, the embodiment of the present invention optimizes and improves the determination operation of the wheel speed error determination box.

[0049] Further, "determine the wheel speed error corresponding to the target time period based on the wheel speed measured by the lidar at the first moment, the posture measured by the lidar at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the lidar at the second moment, the posture measured by the lidar at the second moment and the wheel speed measured by the wheel speed meter at the second moment" is refined into "determine the wheel speed error at the first moment based on the wheel speed measured by the lidar at the first moment, the posture measured by the lidar at the first moment and the wheel speed measured by the wheel speed meter at the first moment; determine the wheel speed error at the second moment based on the wheel speed measured by the lidar at the second moment, the posture measured by the lidar at the second moment and the wheel speed measured by the wheel speed meter at the second moment; determine the wheel speed error corresponding to the target time period based on the wheel speed error at the first moment and the wheel speed error at the second moment" to improve the operation of determining the wheel speed error.

[0050] It should be noted that, for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions in the aforementioned embodiments.

[0051] See also Figure 2 The method for determining the odometer state shown includes:

[0052] S201, obtaining map point data, point cloud data within a target time period, measurement data within a target time period, wheel speed measured by a tachometer within a target time period, lidar measurement data at a first moment, and lidar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement posture.

[0053] S202. Determine the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment, the posture measured by the laser radar at the first moment, and the wheel speed measured by the speedometer at the first moment.

[0054] In this embodiment, the wheel speed error at the first moment may refer to the wheel speed error between the wheel speed measured by the laser radar and the wheel speed measured by the wheel speed meter at the first moment. Specifically, a certain algorithm is used to determine the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment, the laser radar measurement posture at the first moment, and the wheel speed measured by the wheel speed meter at the first moment.

[0055] Optionally, the coordinate system corresponding to the wheel speed measured by the tachometer is the carrier coordinate system; the wheel speed error at the first moment is determined according to the wheel speed measured by the lidar at the first moment, the lidar measurement posture at the first moment and the wheel speed measured by the tachometer at the first moment, including: according to the lidar measurement wheel speed at the first moment and the lidar measurement posture at the first moment, coordinate conversion is performed on the wheel speed measured by the lidar at the first moment to obtain the lidar measurement wheel speed at the first moment in the carrier coordinate system; the wheel speed error at the first moment is determined according to the lidar measurement wheel speed at the first moment in the carrier coordinate system and the wheel speed measured by the tachometer at the first moment.

[0056] Among them, the carrier coordinate system can be a coordinate system with the carrier as the coordinate origin; the coordinate system corresponding to the wheel speed measured by the laser radar can be a world coordinate system, that is, a coordinate system with a fixed object other than the carrier as the coordinate origin. Specifically, a certain algorithm is used to transform the wheel speed measured by the laser radar at the first moment according to the wheel speed measured by the laser radar at the first moment and the laser radar measurement posture at the first moment, so as to obtain the wheel speed measured by the laser radar at the first moment in the carrier coordinate system; the difference between the wheel speed measured by the laser radar at the first moment in the carrier coordinate system and the wheel speed measured by the wheel speedometer at the first moment is determined as the wheel speed error at the first moment.

[0057] Exemplarily, the wheel speed error at the first moment can be expressed by the following formula:

[0058] r s =v rs -v ms ;

[0059] Among them, r s represents the wheel speed error at the first moment; v rs v represents the wheel speed measured by the lidar at the first moment in the carrier coordinate system; ms Indicates the first moment when the tachometer measures the wheel speed.

[0060] It can be understood that by adopting the above technical solution, the wheel speed measured by the lidar at the first moment can be converted to the carrier coordinate system, thereby measuring the wheel speed by the lidar; based on the wheel speed measured by the lidar at the first moment in the carrier coordinate system and the wheel speed measured by the speedometer at the first moment, the wheel speed error at the first moment can be determined.

[0061] Optionally, the laser radar measurement posture is represented in the form of a matrix; according to the laser radar measurement wheel speed at the first moment and the laser radar measurement posture at the first moment, the laser radar measurement wheel speed at the first moment is transformed into coordinates to obtain the laser radar measurement wheel speed at the first moment in the carrier coordinate system, including: multiplying the laser radar measurement posture at the first moment by the laser radar measurement wheel speed at the first moment to obtain the multiplication result; and determining the multiplication result as the laser radar measurement wheel speed at the first moment in the carrier coordinate system.

[0062] Exemplarily, the wheel speed measured by the laser radar at the first moment can be expressed by the following formula:

[0063]

[0064] in, V represents the transposed matrix of the laser radar measurement attitude at the first moment; s Indicates the wheel speed measured by the lidar at the first moment.

[0065] It can be understood that, by adopting the above technical solution, the multiplication result is obtained by multiplying the laser radar measured posture at the first moment with the laser radar measured wheel speed at the first moment; the multiplication result is determined as the laser radar measured wheel speed at the first moment in the carrier coordinate system, thereby improving the accuracy of the laser radar measured wheel speed at the first moment in the carrier coordinate system.

[0066] S203, determining the wheel speed error at the second moment according to the wheel speed measured by the laser radar at the second moment, the posture measured by the laser radar at the second moment, and the wheel speed measured by the speedometer at the second moment.

[0067] It should be noted that the process of determining the wheel speed error at the second moment is similar to the process of determining the wheel speed error at the first moment, and will not be repeated here.

[0068] S204: Determine the wheel speed error corresponding to the target time period according to the wheel speed error at the first moment and the wheel speed error at the second moment.

[0069] In this embodiment, the wheel speed error corresponding to the target time period can be expressed in the form of a matrix. Specifically, the matrix composed of the wheel speed error at the first moment and the wheel speed error at the second moment can be determined as the wheel speed error corresponding to the target time period. Exemplarily, the wheel speed error corresponding to the target time period can be expressed by the following formula:

[0070]

[0071] Among them, r ws Indicates the wheel speed error corresponding to the target time period; r e represents the wheel speed error at the second moment; v represents the transposed matrix of the laser radar measurement attitude at the second moment; e represents the laser radar measurement error at the second moment; v me The wheel speed measured by the wheel speedometer at the second moment.

[0072] S205. Solve the odometer state based on the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the tachometer within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment to obtain the odometer state result corresponding to the target time period.

[0073] The technical solution of the embodiment of the present invention determines the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment, the measurement posture of the laser radar at the first moment and the wheel speed measured by the wheel speed meter at the first moment; determines the wheel speed error at the second moment according to the wheel speed measured by the laser radar at the second moment, the measurement posture of the laser radar at the second moment and the wheel speed measured by the wheel speed meter at the second moment; determines the wheel speed error corresponding to the target time period according to the wheel speed error at the first moment and the wheel speed error at the second moment, thereby improving the accuracy of the wheel speed error corresponding to the target time period.

[0074] Embodiment 3

[0075] Figure 3A This is a flow chart of a method for determining an odometer state provided in Embodiment 3 of the present invention. Based on the technical solutions of the above embodiments, this embodiment of the present invention optimizes and improves the operation of determining an odometer result.

[0076] Further, "solving the odometer state according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the wheel speedometer within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment, and obtaining the odometer state result corresponding to the target time period" is refined into "determining the point cloud map matching error according to the target point cloud data, the map point data and the lidar measurement data at the first moment; determining the IMU pre-integration error according to the IMU measurement data within the target time period, the lidar measurement data at the first moment and the lidar measurement data at the second moment; determining the wheel speedometer pre-integration error according to the wheel speed measured by the wheel speedometer within the target time period, the IMU measurement data within the target time period, the lidar measurement data at the first moment and the lidar measurement data at the second moment; determining the total error between the point cloud map matching error, the IMU pre-integration error and the wheel speedometer pre-integration; solving the odometer with the goal of minimizing the total error to obtain the odometer result" to improve the odometer result determination operation.

[0077] It should be noted that, for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions in the aforementioned embodiments.

[0078] See also Figure 3A The method for determining the odometer state shown includes:

[0079] S301. Acquire point cloud data within a target time period, IMU measurement data within a target time period, wheel speed measured by a tachometer within a target time period, lidar measurement data at a first moment, and lidar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement posture.

[0080] S302, determine the wheel speed error corresponding to the target time period based on the wheel speed measured by the laser radar at the first moment, the posture measured by the laser radar at the first moment, the wheel speed measured by the speedometer at the first moment, the wheel speed measured by the laser radar at the second moment, the posture measured by the laser radar at the second moment, and the wheel speed measured by the speedometer at the second moment.

[0081] S303: Determine the point cloud map matching error according to the target point cloud data, the map point data and the laser radar measurement data at the first moment.

[0082] In this embodiment, the point cloud map matching error may refer to the matching error of performing map matching on the point cloud data. Specifically, a certain algorithm is used to determine the point cloud map matching error according to the target point cloud data and the laser radar measurement data at the first moment.

[0083] Optionally, the target point cloud data includes the position of at least one acquisition point; the map point data includes the position of at least one map point in the map coordinate system; the point cloud map matching error is determined based on the target point cloud data, the map point data and the lidar measurement data at the first moment, including: projecting each acquisition point into the map coordinate system according to the lidar measurement posture at the first moment, to obtain a map projection point corresponding to each acquisition point; performing motion compensation on each map projection point according to the lidar measurement position at the first moment, to obtain a motion compensation point corresponding to each acquisition point; for each motion compensation point, determining an adjacent plane of the motion compensation point in the map coordinate system according to the position of the motion compensation point and the position of at least one map point in the map coordinate system; determining the distance between the motion compensation point and the adjacent plane corresponding to the motion compensation point; and determining the point cloud map matching error according to the distances corresponding to each motion compensation point.

[0084] The acquisition point can be a point in the point cloud corresponding to the point cloud data; the map point can be a point in the map coordinate system. The map projection point is the point projected from the acquisition point to the map coordinate system. The motion compensation point is the point after motion compensation of the map projection point. The adjacent plane can be a plane composed of map points adjacent to the map projection.

[0085] Specifically, for each acquisition point, according to the laser radar measurement posture at the first moment, the acquisition point is projected into the map coordinate system to obtain the map projection point corresponding to the acquisition point; according to the laser radar measurement position at the first moment, the map projection point is motion compensated to obtain the motion compensation point corresponding to the acquisition point; illustratively, the position of the motion compensation point can be expressed by the following formula:

[0086]

[0087] in, Indicates the position of the motion compensation point; R s represents the laser radar measurement attitude at the first moment; p s Indicates the location of the collection point; t s Indicates the lidar measurement position at the first moment.

[0088] For each motion compensation point, according to the position of the motion compensation point and the position of at least one map point in the map coordinate system, a nearest neighbor search algorithm is used to search for three map points closest to the motion compensation point in the map coordinate system; if the three searched map points can form a valid plane, the valid plane is determined as the adjacent plane; if the valid plane cannot be formed, the map points are searched repeatedly until a valid plane can be determined; the distance between the motion compensation point and its corresponding adjacent plane is determined; illustratively, the distance can be determined by the following formula:

[0089]

[0090] n=(p 1 -p 2 )×(p 1 -p 3 );

[0091] Where h represents the distance; n represents the normal vector of the adjacent plane; p 1 Indicates the location of the first map point found; p 2 Indicates the location of the second map point found; p 3 Indicates the location of the third map point found.

[0092] The average value of the distances corresponding to the motion compensation points is used to determine the point cloud map matching error.

[0093] It can be understood that, by adopting the above technical solution, each acquisition point is projected into the map coordinate system to obtain the map projection point corresponding to each acquisition point, and each map projection point is motion compensated to obtain the motion compensation point corresponding to each acquisition point; for each motion compensation point, the distance between the motion compensation point and its corresponding adjacent plane is determined; the average value between each distance is determined as the point cloud map matching error, thereby improving the accuracy of the point cloud map matching error.

[0094] Optional, Figure 3B It is a schematic diagram of the distance between a point and a plane. Figure 3B As shown, the gray points are map points in the map coordinate system; the red points are motion compensation points; the blue points are map points closest to the motion compensation points; and the distance between the plane formed by the motion compensation points and the blue points is h.

[0095] S304, determining an IMU pre-integration error according to the IMU measurement data within the target time period, the lidar measurement data at the first moment, and the lidar measurement data at the second moment.

[0096] In this embodiment, the IMU pre-integration error can be expressed in the form of a matrix. The IMU pre-integration error may include but is not limited to position pre-integration error, attitude pre-integration error, angular velocity zero bias error and acceleration zero bias error; illustratively, the IMU pre-integration error can be expressed by the following formula:

[0097]

[0098] Where r1 represents the IMU pre-integration error; r α Represents the position pre-integration error; r δ represents the attitude pre-integration error; r ω Represents the angular velocity zero bias error; r a Indicates the acceleration zero bias error; t e represents the laser radar measurement position at the second moment; t s Represents the laser radar measurement position at the first moment; R′ s Represents the posture measured by the IMU unit at the first moment; α se represents the IMU position pre-integration; v′ s Indicates the wheel speed measured by the IMU unit at the first moment; Δt se represents the interval between the first moment and the second moment; g represents the acceleration due to gravity; R e represents the laser radar measurement attitude at the second moment; Represents the transposed matrix of the laser radar measurement attitude at the first moment; δ se represents the IMU attitude pre-integration error; b ωe represents the angular velocity zero bias at the second moment; bωs represents the angular velocity zero bias at the first moment; b ae Indicates the acceleration zero bias at the second moment; b as represents the acceleration zero bias at the second moment; Δt represents the IMU measurement interval, that is, the interval between the kth moment and the k+1th moment; represents the acceleration measured by the IMU unit at the kth moment; b ak Indicates the acceleration zero bias measured by the IMU unit at the kth moment; n ak represents the acceleration noise measured by the IMU unit at the kth moment; β sk represents the IMU wheel speed pre-integration between the first moment and the kth moment; δ sj represents the attitude pre-integral between the first moment and the jth moment; represents the acceleration measured by the IMU unit at the jth moment; b aj Indicates the acceleration zero bias measured by the IMU unit at the jth moment; n aj represents the acceleration noise measured by the IMU at the jth moment; δ sk represents the attitude pre-integral between the first moment and the kth moment; Represents the transposed matrix of the attitude measured by the IMU unit at the first moment; R′ k Represents the attitude measured by the IMU unit at the kth moment.

[0099] S305, determining a wheel speed meter pre-integration error according to the wheel speed measured by the wheel speed meter within the target time period, the IMU measurement data within the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment.

[0100] In this embodiment, the wheel speed meter pre-integration error may be a wheel speed meter pre-integration error. Specifically, a certain algorithm is used to determine the wheel speed meter pre-integration error according to the wheel speed meter measurement data within the target time period, the IMU measurement data within the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment.

[0101] Optionally, a wheel speed meter pre-integration error is determined based on the wheel speed measured by the wheel speed meter within the target time period, the IMU measurement data within the target time period, the lidar measurement data at the first moment and the lidar measurement data at the second moment, including: determining the position pre-integration of the wheel speed meter based on the wheel speed measured by the wheel speed meter within the target time period and the IMU measurement data within the target time period; determining the position increment in the carrier coordinate system based on the lidar measurement position at the first moment, the lidar measurement position at the second moment and the lidar measurement posture at the first moment; determining the wheel speed meter pre-integration error based on the position increment and the position pre-integral of the wheel speed meter.

[0102] Exemplarily, the position pre-integration of the wheel speed meter can be expressed by the following formula:

[0103]

[0104] Among them, η se represents the position pre-integration based on wheel speed; v wk represents the wheel speed measured by the wheel speed meter at the kth moment; Δt″ represents the measurement interval of the wheel speed meter.

[0105] Exemplarily, the position increment in the carrier coordinate system may be determined by the following formula:

[0106]

[0107] Wherein, Q represents the position increment in the carrier coordinate system;

[0108] Exemplarily, the wheel speed meter pre-integration error may be determined by the following formula:

[0109] r2=Q-η se ;

[0110] Where r2 represents the pre-integration error of the wheel speed meter.

[0111] It can be understood that, by adopting the above technical scheme, the position pre-integral of the wheel speed meter is determined based on the wheel speed measured by the wheel speed meter within the target time period and the IMU measurement data within the target time period; the position increment in the carrier coordinate system is determined based on the lidar measurement position at the first moment, the lidar measurement position at the second moment and the lidar measurement posture at the first moment; the wheel speed meter pre-integration error is determined based on the position increment and the position pre-integral of the wheel speed meter, thereby improving the accuracy of the wheel speed meter pre-integration error.

[0112] S306: Determine the total error among the wheel speed error, the point cloud map matching error, the IMU pre-integration error and the wheel speed meter pre-integration error.

[0113] S307, with the goal of minimizing the total error, solve the odometer to obtain the odometer result.

[0114] In a specific implementation, the odometer can be solved by a nonlinear optimization algorithm with the goal of minimizing the total error to obtain an odometer result. The present invention does not limit the type of nonlinear optimization algorithm. For example, the nonlinear optimization algorithm can be a LM (Levenberg-Marquardt) algorithm.

[0115] Optional, Figure 3C This is a flow chart of the operation of an odometer system. Figure 3CAs shown in the figure, the input data is the point cloud data collected by the LiDAR sensor, the data measured by the IMU unit, and the wheel speed data measured by the wheel speed meter. The data collected by the IMU unit and the wheel speed meter will be integrated, and pre-integrated on the basis of the integration. Since the sampling frequency of the wheel speed meter of the IMU unit is much higher than that of the LiDAR, the integration and pre-integration processing is performed on the inertial measurement values ​​within the interval between two consecutive frames of point cloud data. The purpose of integration is to obtain the relative motion measured by the inertial sensor within the sampling period of the LiDAR to achieve motion compensation of the point cloud data. The purpose of pre-integration is to integrate the inertial measurement data within the range into a measurement value related only to the initial state, avoiding repeated integration operations during the optimization process. The purpose of system initialization is to achieve the alignment of the coordinate systems of each sensor, and to initialize the relevant states and local map data at the same time. After the system is initialized, the odometer system is officially put into operation, in which the point cloud data after downsampling and motion compensation is matched with the local map data through the nearest neighbor search algorithm, and the point cloud matching error is calculated by calculating the distance from the point to the surface; the present invention discretizes the posture of the laser radar, and fuses the discretized state with the pre-integration result and the measurement result of the wheel speed meter respectively to realize the calculation of the pre-integration error and the wheel speed error; the point cloud matching error, the pre-integration error and the wheel speed error are fused to realize the construction of multi-source constraints, and the odometer is solved by solving the nonlinear optimization problem.

[0116] The embodiment of the present invention determines the point cloud map matching error according to the target point cloud data, the map point data and the laser radar measurement data at the first moment; determines the IMU pre-integration error according to the IMU measurement data within the target time period, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment; determines the wheel speed meter pre-integration error according to the wheel speed measured by the wheel speed meter within the target time period, the IMU measurement data within the target time period, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment; determines the total error among the wheel speed error, the point cloud map matching error, the IMU pre-integration error and the wheel speed meter pre-integration; solves the odometer with the goal of minimizing the total error to obtain the odometer result, thereby improving the accuracy of the odometer result.

[0117] Embodiment 4

[0118] Figure 4 A schematic diagram of the structure of a device for determining an odometer state provided in Embodiment 4 of the present invention. This embodiment can be applied to the case of solving an odometer state, the device can execute an odometer state determination method, the odometer state determination device can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device.

[0119] See also Figure 4The device for determining the odometer state shown includes a data acquisition module 401, an error determination module 402 and an odometer solution module 403, wherein:

[0120] The data acquisition module 401 is used to acquire map point data, point cloud data within a target time period, IMU measurement data within a target time period, wheel speed measured by a wheel speed meter within a target time period, laser radar measurement data at a first moment, and laser radar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the laser radar measurement data includes laser radar measurement position, laser radar measurement wheel speed, and laser radar measurement posture;

[0121] The error determination module 402 is used to determine the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the attitude measured by the laser radar at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the attitude measured by the laser radar at the second moment, and the wheel speed measured by the wheel speed meter at the second moment;

[0122] The odometer solution module 403 is used to solve the odometer state according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the wheel speed meter within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment, and obtain the odometer state result corresponding to the target time period.

[0123] The technical solution of the embodiment of the present invention measures the wheel speed by using a laser radar to measure the wheel speed, the laser radar to measure the posture and the wheel speed by using a speedometer to measure the wheel speed, and then solves the odometer state based on the wheel speed error, the IMU measurement data within the target time period, the target point cloud data, the map point data, the laser radar measurement data at the first moment and the laser radar measurement data at the second moment. When solving the odometer state, the wheel speed error can be constrained, thereby improving the accuracy of the wheel speed and other parameters in the odometer state.

[0124] Optionally, the error determination module 402 includes:

[0125] a first wheel speed error determination unit, configured to determine the wheel speed error at a first moment according to the wheel speed measured by the laser radar at a first moment, the posture measured by the laser radar at the first moment, and the wheel speed measured by the wheel speed meter at the first moment;

[0126] a second wheel speed error determination unit, configured to determine the wheel speed error at the second moment according to the wheel speed measured by the laser radar at the second moment, the posture measured by the laser radar at the second moment, and the wheel speed measured by the wheel speed meter at the second moment;

[0127] The third wheel speed error determining unit is used to determine the wheel speed error corresponding to the target time period according to the wheel speed error at the first moment and the wheel speed error at the second moment.

[0128] Optionally, the coordinate system corresponding to the wheel speed measured by the wheel speed meter is the carrier coordinate system;

[0129] The first wheel speed error determination unit includes:

[0130] A coordinate conversion subunit is used to perform coordinate conversion on the wheel speed measured by the laser radar at the first moment according to the wheel speed measured by the laser radar at the first moment and the attitude measured by the laser radar at the first moment, so as to obtain the wheel speed measured by the laser radar at the first moment in the carrier coordinate system;

[0131] The first wheel speed error determination subunit is used to determine the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment in the carrier coordinate system and the wheel speed measured by the wheel speed meter at the first moment.

[0132] Optionally, the LiDAR measurement attitude is represented in the form of a matrix;

[0133] The first wheel speed error determination subunit is specifically used for:

[0134] Multiplying the laser radar measured attitude at the first moment by the laser radar measured wheel speed at the first moment to obtain a multiplication result;

[0135] The multiplication result is determined as the wheel speed measured by the lidar at the first moment in the carrier coordinate system.

[0136] Optional, odometer solution module, including:

[0137] A point cloud map matching error determination unit, used to determine a point cloud map matching error according to target point cloud data, map point data and laser radar measurement data at a first moment;

[0138] A first pre-integration error determination unit is used to determine an IMU pre-integration error according to the IMU measurement data within the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment;

[0139] A second pre-integration error determination unit is used to determine a wheel speed meter pre-integration error according to the wheel speed measured by the wheel speed meter within the target time period, the IMU measurement data within the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment;

[0140] A total error determination unit, used to determine the total error between the point cloud map matching error, the IMU pre-integration error and the wheel speed meter pre-integration;

[0141] The solving unit is used to solve the odometer with the goal of minimizing the total error and obtain the odometer result.

[0142] Optionally, the target point cloud data includes the position of at least one point; the map point data includes the position of at least one map point in a map coordinate system;

[0143] Point cloud map matching error determination unit, including:

[0144] A motion compensation subunit, used to perform motion compensation on the position of each point according to the laser radar measurement data at the first moment and the position of each point, so as to obtain a motion compensation point corresponding to each point;

[0145] A projection subunit, used for projecting each motion compensation point into a map coordinate system to obtain a map projection point;

[0146] a plane determination subunit, for determining, for each ground projection point, a neighboring plane of the map projection point in the map coordinate system according to a position of the map projection point and a position of at least one map point in the map coordinate system;

[0147] A distance determination subunit, used for determining the distance between a map projection point and an adjacent plane corresponding to the map projection point;

[0148] The matching error determination subunit is used to determine the point cloud map matching error according to the distances corresponding to the ground projection points.

[0149] Optionally, the second pre-integration error determining unit includes:

[0150] A position pre-integration determination subunit, used to determine the position pre-integral of the wheel speed meter according to the wheel speed meter measured in the target time period and the IMU measurement data in the target time period;

[0151] A position increment determination subunit, used to determine the position increment in the carrier coordinate system according to the laser radar measurement position at the first moment, the laser radar measurement position at the second moment and the laser radar measurement posture at the first moment;

[0152] The second pre-integration error determination subunit is used to determine the wheel speed meter pre-integration error according to the position increment and the position pre-integration of the wheel speed meter.

[0153] The device for determining the odometer status provided in the embodiment of the present invention can execute the method for determining the odometer status provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method for determining the odometer status.

[0154] Embodiment 5

[0155] Figure 5A schematic diagram of the structure of an electronic device 500 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0156] like Figure 5 As shown, the electronic device 500 includes at least one processor 501, and a memory connected to the at least one processor 501 in communication, such as a read-only memory (ROM) 502, a random access memory (RAM) 503, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 501 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 to the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0157] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0158] The processor 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 501 performs the various methods and processes described above, such as a method for determining the odometer state.

[0159] In some embodiments, the method for determining the odometer state may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the processor 501, one or more steps of the method for determining the odometer state described above may be performed. Alternatively, in other embodiments, the processor 501 may be configured to execute the method for determining the odometer state in any other appropriate manner (e.g., by means of firmware).

[0160] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0161] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable odometer state determination device, so that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0162] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0163] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0164] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0165] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0166] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0167] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining an odometer state, characterized in that: The method comprises: Obtain map point data, point cloud data within a target time period, IMU (Inertial Measurement Unit) measurement data within the target time period, wheel speed measured by a wheel speed meter within the target time period, lidar measurement data at a first moment, and lidar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the lidar measurement data includes the lidar measurement position, the lidar measurement wheel speed, and the lidar measurement attitude; Determine the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the laser radar measurement posture at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the laser radar measurement posture at the second moment, and the wheel speed measured by the wheel speed meter at the second moment; The odometer state is solved according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the speedometer within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment to obtain the odometer state result corresponding to the target time period.

2. The method according to claim 1, characterized in that The determining the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the laser radar measurement posture at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the laser radar measurement posture at the second moment, and the wheel speed measured by the wheel speed meter at the second moment includes: Determine the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment, the posture measured by the laser radar at the first moment, and the wheel speed measured by the wheel speed meter at the first moment; Determine the wheel speed error at the second moment according to the wheel speed measured by the laser radar at the second moment, the posture measured by the laser radar at the second moment, and the wheel speed measured by the wheel speed meter at the second moment; The wheel speed error corresponding to the target time period is determined according to the wheel speed error at the first moment and the wheel speed error at the second moment.

3. The method according to claim 2, characterized in that The coordinate system corresponding to the wheel speed measured by the wheel speed meter is the carrier coordinate system; The determining the wheel speed error at the first moment according to the wheel speed measured by the laser radar at the first moment, the posture measured by the laser radar at the first moment, and the wheel speed measured by the wheel speed meter at the first moment includes: According to the wheel speed measured by the laser radar at the first moment and the attitude measured by the laser radar at the first moment, coordinate conversion is performed on the wheel speed measured by the laser radar at the first moment to obtain the wheel speed measured by the laser radar at the first moment in the carrier coordinate system; The wheel speed error at the first moment is determined according to the wheel speed measured by the laser radar at the first moment in the carrier coordinate system and the wheel speed measured by the wheel speedometer at the first moment.

4. The method according to claim 3, characterized in that The laser radar measurement posture is represented in the form of a matrix; The step of performing coordinate conversion on the wheel speed measured by the laser radar at the first moment according to the wheel speed measured by the laser radar at the first moment and the attitude measured by the laser radar at the first moment to obtain the wheel speed measured by the laser radar at the first moment in the carrier coordinate system includes: Multiplying the laser radar measured posture at the first moment by the laser radar measured wheel speed at the first moment to obtain a multiplication result; The multiplication result is determined as the wheel speed measured by the laser radar at the first moment in the carrier coordinate system.

5. The method according to claim 1, characterized in that The odometer state is solved according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the wheel speed meter within the target time period, the target point cloud data, the map point data, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment to obtain the odometer state result corresponding to the target time period, including: Determining a point cloud map matching error based on the target point cloud data, the map point data, and the laser radar measurement data at the first moment; Determine an IMU pre-integration error according to the IMU measurement data within the target time period, the lidar measurement data at the first moment, and the lidar measurement data at the second moment; Determine a wheel speed meter pre-integration error according to the wheel speed measured by the wheel speed meter within the target time period, the IMU measurement data within the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment; Determining a total error between the wheel speed error, the point cloud map matching error, the IMU pre-integration error, and the wheel speed meter pre-integration; With the goal of minimizing the total error, the odometer solution is performed to obtain the odometer result.

6. The method according to claim 5, characterized in that The point cloud data includes the position of at least one acquisition point; the map point data includes the position of at least one map point in a map coordinate system; The determining the point cloud map matching error according to the target point cloud data, the map point data and the laser radar measurement data at the first moment includes: According to the laser radar measurement posture at the first moment, each acquisition point is projected into the map coordinate system to obtain the map projection point corresponding to each acquisition point; According to the laser radar measurement position at the first moment, motion compensation is performed on each map projection point to obtain the motion compensation point corresponding to each acquisition point; For each motion compensation point, determining a neighboring plane of the motion compensation point in the map coordinate system according to a position of the motion compensation point and a position of at least one map point in the map coordinate system; Determine the distance between the motion compensation point and an adjacent plane corresponding to the motion compensation point; The point cloud map matching error is determined according to the distances corresponding to the motion compensation points.

7. The method according to claim 5, characterized in that The determining the wheel speed meter pre-integration error according to the wheel speed meter measured in the target time period, the IMU measurement data in the target time period, the laser radar measurement data at the first moment, and the laser radar measurement data at the second moment includes: Determining a position pre-integral of the wheel speed meter according to the wheel speed meter measured within the target time period and the IMU measurement data within the target time period; Determine a position increment in a carrier coordinate system according to the laser radar measurement position at the first moment, the laser radar measurement position at the second moment, and the laser radar measurement posture at the first moment; The wheel speed meter pre-integration error is determined according to the position increment and the position pre-integral of the wheel speed meter.

8. A device for determining an odometer state, characterized in that: The device comprises: A data acquisition module, used to acquire map point data, point cloud data within a target time period, IMU (Inertial Measurement Unit) measurement data within the target time period, wheel speed measured by a wheel speed meter within the target time period, laser radar measurement data at a first moment, and laser radar measurement data at a second moment; wherein the first moment is the start moment of the target time period; the second moment is the end moment of the target time period; the laser radar measurement data includes laser radar measurement position, laser radar measurement wheel speed, and laser radar measurement posture; an error determination module, for determining the wheel speed error corresponding to the target time period according to the wheel speed measured by the laser radar at the first moment, the attitude measured by the laser radar at the first moment, the wheel speed measured by the wheel speed meter at the first moment, the wheel speed measured by the laser radar at the second moment, the attitude measured by the laser radar at the second moment, and the wheel speed measured by the wheel speed meter at the second moment; The odometer solution module is used to solve the odometer state according to the wheel speed error, the IMU measurement data within the target time period, the wheel speed measured by the wheel speed meter within the target time period, the target point cloud data, the map point data, the lidar measurement data at the first moment and the lidar measurement data at the second moment, so as to obtain the odometer state result corresponding to the target time period.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the odometer state according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the odometer state according to any one of claims 1 to 7 when executed.

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