Parameter Calibration Method, Device and Readable Storage Medium of Single-Rudder Wheel Moving Device

Abstract

The present application discloses a parameter calibration method, device and readable storage medium for a single-steering-wheel mobile device. The method includes: obtaining a lidar data sequence and a steering-wheel encoding data sequence generated when the single-steering-wheel mobile device to be calibrated moves along a set path; integrating the lidar data sequence and the steering-wheel encoding data sequence to obtain a steering-wheel movement data sequence; using the equality of the first rotation angle calculated based on the initial steering-wheel yaw angle and the average steering-wheel rotation speed in the same steering-wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition to determine the steering-wheel zero offset value in the steering-wheel encoder internal parameters; determining the steering-wheel radius and vehicle body wheelbase in the steering-wheel encoder internal parameters based on the translation constraint condition of hand-eye calibration, and determining the external parameters between the lidar and the steering-wheel encoder. Through the above method, the present application can realize the joint calibration of the external parameters and the encoder internal parameters of the single-steering-wheel mobile device, with high accuracy, small error, and simple and reliable implementation method.

Description

Technical Field

[0001] This application relates to the field of mobile robot calibration, and in particular to a method, device, and readable storage medium for calibrating parameters of a single-steering-wheel mobile device. Background Art

[0002] Mobile robots equipped with multiple sensors are widely used for their high efficiency and high precision. Mobile robots usually fuse data from multiple sensors for positioning. For example, lidar and encoders are two of the most commonly used sensors in mobile robots. In practical applications, for the convenience of assembly, the two sensors are assembled at different positions on the mobile robot, and their coordinate systems do not overlap. It is necessary to calibrate the coordinate systems of the two sensors and the internal parameters of the encoder. Summary of the Invention

[0003] This application mainly provides a method, device, and readable storage medium for calibrating parameters of a single-steering-wheel mobile device, which solves the problem of low calibration accuracy of single-steering-wheel mobile devices in the prior art.

[0004] To solve the above technical problems, a first aspect of this application provides a method for calibrating parameters of a single-steering-wheel mobile device, including: obtaining a lidar data sequence and a steering-wheel encoder data sequence generated by the single-steering-wheel mobile device to be calibrated moving along a set path; integrating the lidar data sequence and the steering-wheel encoder data sequence to obtain a steering-wheel movement data sequence, where the steering-wheel movement data sequence includes multiple groups of steering-wheel movement data arranged in chronological order, and each group of steering-wheel movement data includes the start time and end time of the steering-wheel movement data determined by the sampling times of two lidar data and lidar pose transformation data, and at least one initial steering-wheel yaw angle and the average steering-wheel rotation speed located between the start time and the end time determined by the steering-wheel encoder data; using the equality of the first rotation angle calculated based on the initial steering-wheel yaw angle and the average steering-wheel rotation speed in the same steering-wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition to determine the steering-wheel zero offset value in the steering-wheel encoder internal parameters; determining the steering-wheel radius and the vehicle body wheelbase in the steering-wheel encoder internal parameters based on the translation constraint condition of hand-eye calibration, and determining the external parameters between the lidar and the steering-wheel encoder.

[0005] To solve the above technical problems, a second aspect of this application provides a calibration device for a single-steering-wheel mobile device. The device includes a processor and a memory coupled to each other; a computer program is stored in the memory, and the processor is configured to execute the computer program to implement the method for calibrating parameters of a single-steering-wheel mobile device provided in the first aspect above.

[0006] To solve the above technical problems, a third aspect of the present application provides a computer-readable storage medium storing program data, which when executed by a processor, implements the parameter calibration method of the single-steering-wheel mobile device provided in the first aspect above.

[0007] The beneficial effects of the present application are as follows: Different from the prior art, the present application obtains a lidar data sequence and a steering wheel encoder data sequence generated when the to-be-calibrated single-steering-wheel mobile device moves along a set path, and then integrates the lidar data sequence and the steering wheel encoder data sequence to obtain a steering wheel movement data sequence. Among them, the steering wheel movement data sequence includes multiple groups of steering wheel movement data arranged in chronological order. Each group of steering wheel movement data includes the start time and end time of the steering wheel movement data determined by the sampling times of two lidar data and the lidar pose transformation data, and at least one initial steering wheel yaw angle and the average steering wheel rotation speed located between the start time and the end time determined by the steering wheel encoder data. Finally, taking the equality of the first rotation angle calculated based on the initial steering wheel yaw angle and the average steering wheel rotation speed in the same steering wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition, the steering wheel zero offset value in the steering wheel encoder internal parameters is determined, and based on the translation constraint condition of hand-eye calibration, the steering wheel radius and the vehicle body wheelbase in the steering wheel encoder internal parameters are determined, and the external parameters between the lidar and the steering wheel encoder are determined. In the above manner, the present application can only obtain the data sequences of the lidar and the steering wheel encoder, realize the joint calibration of the external parameters and the encoder internal parameters of the single-steering-wheel mobile device, with high accuracy, small error, and a simple and reliable implementation method. Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0009] Figure 1 It is a schematic structural diagram of an embodiment of the single-steering-wheel mobile device of the present application;

[0010] Figure 2 It is a schematic flowchart of an embodiment of the parameter calibration method of the single-steering-wheel mobile device of the present application;

[0011] Figure 3 It is a schematic flowchart of an embodiment of step S13 of the present application;

[0012] Figure 4 It is a schematic flowchart of an embodiment of optimizing the external parameters of the present application;

[0013] Figure 5 is a structural schematic block diagram of an embodiment of the calibration device for the steering wheel moving device of the present application;

[0014] Figure 6 is a structural schematic block diagram of another embodiment of the calibration device for the single steering wheel moving device of the present application

[0015] Figure 7 is a structural schematic block diagram of an embodiment of the computer-readable storage medium of the present application. Specific embodiments

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0017] The terms "first" and "second" in the present application are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0018] Referring to "embodiment" in this context means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0019] The calibration method provided herein is used for parameter calibration of a single steering wheel device, and the single steering wheel device is a movable device including one steering wheel. Please refer to Figure 1, the single-steering-wheel mobile device includes a steering wheel A and two driven wheels B and C. The single-steering-wheel device defines the angle by which the steering wheel A deviates from the due front as the yaw angle θ, and the perpendicular distance between the steering wheel A and the line connecting the two driven wheels B and C is the vehicle body wheelbase b.

[0020] Please refer to Figure 2 , Figure 2 is a flow schematic block diagram of an embodiment of the parameter calibration method for the single-steering-wheel mobile device of this application. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 2 the flow sequence shown. This embodiment includes the following steps:

[0021] Step S11: Obtain the lidar data sequence and the steering wheel encoding data sequence generated when the single-steering-wheel mobile device to be calibrated moves along a set path.

[0022] In this step, by controlling the single-steering-wheel mobile device to move along a set path, and during the movement of the steering wheel mobile device, controlling the lidar sensor to emit laser light, synchronously collecting the lidar point cloud data, and collecting the steering wheel encoding data sequence generated by the steering wheel encoder, and matching the point cloud data to obtain the lidar data sequence.

[0023] The set path may include, for example, a straight path and a curved path. For example, it is an "8"-shaped closed path, which includes both a straight path and a curved path, and the obtained data sequence is more comprehensive.

[0024] Among them, the point cloud data sequence can be expressed as: {S1, S2,..., S n}, and each frame of point cloud data in the point cloud data sequence includes at least the sampling timestamp t and the lidar point cloud C.

[0025] In this step, the registration of two frames of point cloud data can be performed at intervals of a set number of frames to obtain the lidar data sequence. For example, starting from the first frame of data in {S1, S2,..., S n}, match every 6 frames, that is, match S1 with S7 to obtain the pose T1 = [Δx l Δy l Δθ l T , the start time and end time corresponding to this pose are the sampling timestamp of S1 and the sampling timestamp of S7 respectively. After that, match S7 with S 13 and so on until the end of the data sequence. Finally, the lidar data sequence {T1, T2,..., T r} can be obtained. Each set of lidar data in the lidar data sequence includes the lidar pose transformation information (Δx l , Δy l , Δθ​l ) and the start time t of the acquisition of this set of data start and the end time t end .

[0026] The steering wheel encoding data sequence can be expressed as {E1, E2,..., E m}}. Each steering wheel encoding data in the steering wheel encoding data sequence includes at least the start time t start , the steering wheel rotation speed n, and the steering wheel yaw angle θ.

[0027] Step S12: Integrate the lidar data sequence and the steering wheel encoding data sequence to obtain the steering wheel movement data sequence.

[0028] Among them, the steering wheel movement data sequence includes multiple groups of steering wheel movement data arranged in chronological order. Each group of steering wheel movement data includes the start time and end time of the steering wheel movement data determined by the sampling moments of two lidar data and the lidar pose transformation data, and the average steering wheel rotation speed and at least one initial steering wheel yaw angle located between the start time and the end time determined by the steering wheel encoding data.

[0029] The steering wheel movement data sequence can be expressed as:

[0030] {D1, D2,..., D r}

[0031] Among them, D i =(T i , {E s , E s+1 ,..., E e}), i = 1…r, {E s , E s+1 ,..., E e} is the steering wheel encoding data sequence corresponding to the start time and the end time. The timestamps of {E s , E s+1 ,..., E e} are respectively the start time t i and the end time t start of T end , e - s ≥ 0, that is, each group of steering wheel encoding data determines at least one group of steering wheel encoding data corresponding to the start time and the end time. Therefore, each group of steering wheel movement data D i includes the start time t start , the end time t end , the laser pose transformation information (Δx l , Δy l , Δθ l ), and the average steering wheel rotation speed n determined by the steering wheel encoding data sequencew and t start the initial steering wheel yaw angle θ at the moment w .

[0032] Before step S12, interpolation can be performed on the steering wheel encoded data sequence so that the t corresponding to each lidar data start and the end time t end both correspond to a steering wheel encoded data. For example, taking the start and end timestamps t of each data in the lidar data sequence start and t end as a reference, the steering wheel encoded data around the timestamps t start and t end is interpolated using a uniform acceleration model to obtain an interpolated steering wheel encoded data sequence.

[0033] Optionally, this embodiment further includes: removing the steering wheel movement data in the steering wheel movement data sequence that does not conform to the set rules.

[0034] In one embodiment, the steering wheel movement data with an average steering wheel rotation speed less than the set speed threshold in the steering wheel movement data sequence can be removed. It can be understood that this data indicates that the device is almost stationary during this time period and can be removed.

[0035] In one embodiment, the steering wheel movement data with the lidar deflection direction opposite to the steering wheel encoder deflection direction in the steering wheel movement data sequence can be removed. That is, when the rotation directions represented by the laser rotation angle Δθ l and the steering wheel encoder rotation angle are opposite, this data indicates that the device is abnormal or there is data delay and can be removed.

[0036] In one embodiment, the steering wheel movement data in the steering wheel movement data sequence with a change in the steering wheel deflection angle from the start time to the end time exceeding the set angle deviation threshold can be removed. Since larger data will cause a large error in the process of encoder trajectory integration and can be removed.

[0037] Step S13: Taking the equality of the first rotation angle calculated based on the initial steering wheel yaw angle and the average steering wheel rotation speed in the same steering wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition, determine the steering wheel zero bias value in the steering wheel encoder internal parameters.

[0038] In a set of steering wheel movement data D i =(T i ,{E s ,E s+1 ,...,E e}), from t start to tend During a time period, the second rotation angle determined from the lidar pose transformation data is Δθ l , and the first rotation angle calculated from the initial steering wheel yaw angle and the average steering wheel rotation speed is Δθ o , then the rotation constraint can be expressed as Δθ l = Δθ o .

[0039] Please refer to Figure 3 , Figure 3 which is a flowchart of an embodiment of step S13 of this application. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 3 the process sequence shown. This embodiment can determine the steering wheel zero offset value in the steering wheel encoder internal parameters according to the following steps:

[0040] Step S131: Construct an equivalent relationship equation using the sine value of the sum of the initial steering wheel yaw angle and the steering wheel zero offset value, the product result of the average steering wheel rotation speed and the steering wheel radius, and the ratio of the product result to the vehicle body wheelbase, and the corresponding second rotation angle.

[0041] Among them, the equivalent relationship equation can be expressed as Equation (1):

[0042]

[0043] Among them, k1 and k2 are parameters to be calculated, θ l , θ w , n w are the second rotation angle, the initial steering wheel yaw angle at the start time, the average steering wheel rotation speed between the start time and the end time, r is the steering wheel radius, b is the vehicle body wheelbase, t end and t start are the end time and the start time respectively.

[0044] Step S132: Determine the steering wheel zero offset value based on at least two sets of equivalent relationship equations.

[0045] Based on at least two sets of steering wheel movement data and the equivalent relationship equations determined in the previous step, a system of equations composed of at least two sets of equivalent relationship equations can be determined. According to the system of equations, the k1 and k2 of the parameters to be calculated can be calculated, and the steering wheel zero offset value can be calculated based on the following formula:

[0046] Among them, the steering wheel movement data includes two steering wheel encoding data corresponding to the start time and the end time, and at least one steering wheel encoding data located between the two steering wheel encoding data. Using a set of steering wheel movement data, the above equivalent relationship equation can be expressed in the form of Equation (2) using the median integration method:

[0047]

[0048] Among them, M is the number of encoder data of the steering wheel, and θ l is the second rotation angle, and t k+1 and t k are the sampling times of the (k + 1)-th and k-th encoder data of the steering wheel respectively. is the average rotational speed of the steering wheel calculated from the rotational speeds k+1 and k of the steering wheel collected at t and , and and are the initial yaw angles of the steering wheel collected at times t k+1 and k .

[0049] In the encoder data sequence of the steering wheel corresponding to a set of steering wheel movement data, the above M is the same as the encoder data in the above {E s , E s+ 1,..., E e}. Understandably, in practical applications, the times represented by t k+1 and k correspond to the time information corresponding to each data in the above encoder data sequence {E s , E s+1 ,..., E e} in the overall time sequence.

[0050] Step S14: Determine the radius of the steering wheel and the wheelbase of the vehicle body in the internal parameters of the steering wheel encoder based on the translation constraint conditions of hand-eye calibration, and determine the external parameters between the lidar and the steering wheel encoder.

[0051] For each set of data D i =(T i , {E s , E s+1 ,..., E e}) within the time period from t start to end , the lidar translation vector is and the encoder translation vector is

[0052] The translation constraint can be expressed in a two-dimensional plane as:

[0053]

[0054] where x o_l is the component of the external parameter between the lidar and the steering wheel encoder in the x direction, and yo_l is the component of the external parameter in the y direction, and θ o_l is the angular component of the external parameter.

[0055] Among them, the translation vector of the encoder needs to be solved. The median integral and closed-form integral methods are used to solve the translation vector of the encoder, which can be expressed in the form of Equation (4):

[0056]

[0057] Where:

[0058]

[0059]

[0060]

[0061] Among them, θ o_k , are all intermediate parameters.

[0062] Furthermore, each data pair in the drive wheel movement data sequence {D1, D2,..., D r} satisfies Equation (3). Expand and simplify it into a matrix to obtain Equation (5):

[0063]

[0064] Construct the following constrained least squares equation:

[0065]

[0066] Where:

[0067] M = ∑Q k T Q k

[0068]

[0069]

[0070] Solving the optimization equation of Equation (6) above, we can obtain:

[0071] Subsequently, use Equation (7) to solve the drive wheel radius in the external parameter:

[0072]

[0073] Different from the prior art, in this embodiment, the steerable wheel movement data sequence is obtained by processing the data sequences of the lidar sensor and the steerable wheel encoder installed on the single-steerable wheel mobile device. According to the performance of the movement rules of the single-steerable wheel mobile device on the data sequence, the internal parameters of the steerable wheel encoder and the external parameters between the lidar and the steerable wheel encoder are calculated, improving the calibration accuracy of the single-steerable wheel mobile device, and it can be achieved only by inputting the data sequences of the lidar sensor and the steerable wheel encoder, which is simple and reliable.

[0074] Using the above method, the initial value of the external parameters (x o_l , y o_l , θ o_l ) between the lidar and the steerable wheel encoder is initially obtained. In this article, the following method can also be used to optimize this initial value of the external parameters.

[0075] Please refer to Figure 4 , Figure 4 which is a flowchart showing a process of optimizing the external parameters in an embodiment of this application. It should be noted that if there are substantially the same results, this embodiment is not limited to the Figure 4 shown process sequence. This embodiment can optimize the external parameters according to the following steps:

[0076] Step S21: Calculate the difference between the product of the laser pose increment matrix corresponding to each steerable wheel movement data and the external parameter matrix constructed by the external parameters and the product of the external parameter matrix and the encoder pose increment matrix.

[0077] Among them, the laser pose increment matrix of each group of steerable wheel movement data can be expressed as: [Δx l Δy l Δθ l T ; The encoder pose increment matrix can be obtained according to the above formula (4) and formula (2), and can be expressed as: [Δx o Δy o Δθ o T . This difference can be expressed as:

[0078] [Δx l Δy l Δθ l T *[x o_l y o_l θ o_l -[x o_l y o_l θ o_l *[Δx o Δy o Δθ o T ​​​​

[0079] Step S22: Optimize the external parameters with the goal of minimizing the sum of the differences corresponding to multiple steering wheel movement data in the steering wheel movement data sequence.

[0080] The optimization function can be expressed as: min∑(AX - XB), where A is the laser pose increment matrix, X is the external parameter matrix, and B is the encoder pose increment matrix.

[0081] This unconstrained optimization can be solved to obtain the optimized external parameters by means such as the least squares method and the gradient method.

[0082] Step S23: Remove the steering wheel movement data whose differences do not meet the determination conditions to obtain a new steering wheel movement data sequence.

[0083] For each steering wheel movement data in the steering wheel movement data sequence, calculate the difference between the product AX of the laser pose increment matrix and the optimized external parameter matrix constructed from the optimized external parameters and the product XB of the optimized external parameter matrix and the encoder pose increment matrix according to AX - XB. If the difference does not meet the preset determination conditions, remove the corresponding steering wheel movement data. The unremoved steering wheel movement data forms a new steering wheel movement data sequence, and the new steering wheel movement data sequence is used to continue the iterative optimization.

[0084] Determine whether the difference meets the preset determination conditions. For example, determine whether the difference exceeds the set difference threshold. If it exceeds, it is determined that the difference does not meet the preset determination conditions.

[0085] Step S24: Return to Step S21.

[0086] Repeat the above Steps S21 - S24 until the iteration end condition is met. The iteration end condition is, for example, that the difference between the current optimization result and the previous optimization result is less than the set value. The iteration end condition can also be set according to actual requirements.

[0087] Different from the prior art, the above method iteratively optimizes the initial value of the external parameters after determining the initial value of the external parameters, thereby obtaining the optimized external parameters, improving the reliability of the algorithm, and further improving the credibility of the external parameters obtained by this method.

[0088] Please refer to Figure 5 , Figure 5It is a structural schematic block diagram of an embodiment of the calibration device for the steering wheel moving device of the present application. The calibration device 100 for the single steering wheel moving device in this embodiment includes: an acquisition module 110, a data processing module 120, and a parameter calculation module 130. Among them, the acquisition module 110 is used to acquire the lidar data sequence and the steering wheel encoding data sequence generated by the single steering wheel moving device to be calibrated moving along a set path. The data processing module 120 is used to integrate the lidar data sequence and the steering wheel encoding data sequence to obtain a steering wheel movement data sequence. The steering wheel movement data sequence includes multiple groups of steering wheel movement data arranged in chronological order. Each group of steering wheel movement data includes the start time and end time of the steering wheel movement data determined by the sampling times of two lidar data and the lidar pose transformation data, and at least one initial steering wheel yaw angle and the average steering wheel rotation speed located between the start time and the end time determined by the steering wheel encoding data. The parameter calculation module 130 is used to take the equality of the first rotation angle calculated based on the initial steering wheel yaw angle and the average steering wheel rotation speed in the same steering wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition to determine the steering wheel zero offset value in the internal parameters of the steering wheel encoder, and determine the steering wheel radius and the vehicle body wheelbase in the internal parameters of the steering wheel encoder based on the translation constraint condition of hand-eye calibration, and determine the external parameters between the lidar and the steering wheel encoder.

[0089] Among them, the calibration device 100 for the single steering wheel moving device can be a processing unit or module connected to the steering wheel moving device itself for data processing, or a processing unit or module that can be connected to the data output interfaces of the lidar sensor and the steering wheel encoder of the single steering wheel moving device.

[0090] For the specific manners of the steps executed by each process, please refer to the descriptions of the steps in the embodiment of the parameter calibration method for the single steering wheel moving device of the present application above, and details will not be repeated here.

[0091] Please refer to Figure 6 , Figure 6 It is a structural schematic block diagram of another embodiment of the calibration device for the single steering wheel moving device of the present application. The calibration device 200 for the single steering wheel moving device includes a processor 210 and a memory 220 that are coupled to each other. A computer program is stored in the memory 220, and the processor 210 is used to execute the computer program to implement the parameter calibration method for the single steering wheel moving device described in the above embodiments.

[0092] For the descriptions of the steps executed by the process, please refer to the descriptions of the steps in the embodiment of the parameter calibration method for the single steering wheel moving device of the present application above, and details will not be repeated here.

[0093] The memory 220 can be used to store program data and modules. The processor 210 executes various functional applications and data processing by running the program data and modules stored in the memory 220. The memory 220 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as data sequence integration processing function, etc.); the data storage area can store data created according to the use of the single-steering-wheel mobile device calibration device 200 (such as lidar data sequence, steering wheel coding data sequence, steering wheel movement data sequence, etc.). In addition, the memory 220 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 220 can also include a memory controller to provide the processor 210 with access to the memory 220.

[0094] In various embodiments of the present application, the disclosed methods and devices can be implemented in other ways. For example, the various embodiments of the single-steering-wheel mobile device calibration device 200 described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, in each embodiment of the present application, the various functional units can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0097] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium.

[0098] Refer to Figure 7 , Figure 7 FIG. is a structural schematic diagram of an embodiment of the computer-readable storage medium of the present application. The computer-readable storage medium 300 stores program data 310, and when the program data 310 is executed, the steps of the parameter calibration method of the single-steering-wheel mobile device in the above embodiments are implemented.

[0099] For the description of each step of the process execution, please refer to the description of each step of the parameter calibration method embodiment of the single-steering-wheel mobile device of the present application above, and details are not described herein again.

[0100] The computer-readable storage medium 300 can be various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc.

[0101] The above are only embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A parameter calibration method for a single-steering-wheel mobile device, characterized in that, The method includes: Obtaining a lidar data sequence and a steering wheel encoder data sequence generated by a single-steering-wheel mobile device to be calibrated moving along a set path; Integrating the lidar data sequence and the steering wheel encoder data sequence to obtain a steering wheel movement data sequence, where the steering wheel movement data sequence includes multiple groups of steering wheel movement data arranged in chronological order. Each group of steering wheel movement data includes the start time and end time of the steering wheel movement data determined by the sampling times of two lidar data and lidar pose transformation data, and the average steering wheel rotation speed and at least one initial steering wheel yaw angle located between the start time and the end time determined by the steering wheel encoder data; Taking the equality of the first rotation angle calculated based on the initial steering wheel yaw angle and the average steering wheel rotation speed in the same steering wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition to determine the steering wheel zero offset value in the steering wheel encoder internal parameters; Determining the steering wheel radius and vehicle body wheelbase in the steering wheel encoder internal parameters based on the translation constraint condition of hand-eye calibration, and determining the external parameters between the lidar and the steering wheel encoder.

2. The method according to claim 1, characterized in that, The step of taking the equality of the first rotation angle calculated based on the initial steering wheel yaw angle and the average steering wheel rotation speed in the same steering wheel movement data and the second rotation angle determined by the lidar pose transformation data as the rotation constraint condition to determine the steering wheel zero offset value in the steering wheel encoder internal parameters includes: Constructing an equivalent relationship equation with the first rotation angle calculated by using the sine value of the sum result of the initial steering wheel yaw angle and the steering wheel zero offset value, the product result of the average steering wheel rotation speed and the steering wheel radius, and the ratio to the vehicle body wheelbase and the corresponding second rotation angle, where the vehicle body wheelbase is the vertical distance between the rotation axis of the steering wheel and the connection line between the center points of two driven wheels; Determining the steering wheel zero offset value based on at least two groups of the equivalent relationship equations.

3. The method according to claim 2, characterized in that, The equivalent relationship equation is expressed as: where k1 and k2 are parameters to be calculated, θ l and θ w and n w are respectively the second rotation angle, the initial steering wheel yaw angle at the start time, the average steering wheel rotation speed between the start time and the end time, r is the steering wheel radius, b is the vehicle body wheelbase, t end and t start are respectively the end time and the start time; The step of determining the steering wheel zero offset value based on at least two groups of the equivalent relationship equations includes: Calculating k1 and k2 of the parameter to be calculated based on at least two groups of the equivalent relationship equations; Calculate the zero offset value of the steering wheel based on the following formula:

4. The method according to claim 2, characterized in that, The steering wheel movement data includes two steering wheel encoder data corresponding to the start time and the end time and at least one steering wheel encoder data located between the two steering wheel encoder data, and the equivalent relationship equation is expressed as: where M is the number of the encoder data of the steering wheel, and θ l is the second rotation angle, t k+1 and t k are the sampling times of the k-th encoder data and the (k + 1)-th encoder data of the steering wheel, is the average rotational speed of the steering wheel calculated from the rotational speeds k+1 and k of the steering wheel collected at t and , and and are the initial yaw angles of the steering wheel collected at t k+1 and k .

5. The method according to claim 1, characterized in that, The method further includes: removing the steering wheel movement data that does not conform to the set rules in the steering wheel movement data sequence.

6. The method according to claim 5, characterized in that, The step of removing the steering wheel movement data that does not conform to the set rules in the steering wheel movement data sequence includes: Removing the steering wheel movement data with an average steering wheel rotation speed less than the set speed threshold in the steering wheel movement data sequence.

7. The method according to claim 5, characterized in that, The step of removing the steering wheel movement data that does not conform to the set rules in the steering wheel movement data sequence includes: Removing the steering wheel movement data with the lidar deflection direction opposite to the steering wheel encoder deflection direction in the steering wheel movement data sequence.

8. The method according to claim 5, characterized in that, The step of removing the steering wheel movement data that does not conform to the set rules in the steering wheel movement data sequence includes: Remove the steering wheel movement data in the steering wheel movement data sequence where the change in the steering wheel deflection angle from the start time to the end time exceeds the set angular deviation threshold.

9. The method according to claim 1, characterized in that, The method further includes: Calculating the difference between the product of the laser pose increment matrix corresponding to each steering wheel movement data and the extrinsic parameter matrix constructed from the extrinsic parameters and the product of the extrinsic parameter matrix and the encoder pose increment matrix; Optimizing the extrinsic parameters with the goal of minimizing the sum of the differences corresponding to multiple steering wheel movement data in the steering wheel movement data sequence; Removing the steering wheel movement data for which the difference does not meet the determination condition to obtain a new steering wheel movement data sequence; Returning to the step of calculating the difference between the product of the laser pose increment matrix corresponding to each steering wheel movement data and the extrinsic parameter matrix constructed from the extrinsic parameters and the product of the extrinsic parameter matrix and the encoder pose increment matrix until the iteration end condition is satisfied.

10. A parameter calibration device for a single-steering-wheel mobile device, characterized in that, The device includes a processor and a memory coupled to each other; the memory stores a computer program, and the processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1-9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data, and when the program data is executed by a processor, the steps of the method according to any one of claims 1-9 are implemented.

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