Joint calibration method and system

Abstract

The invention discloses a joint calibration method and system, belongs to the technical field of surveying and mapping, automatically calculates and optimizes space calibration parameters of equipment through data fusion of a laser radar and an ultra-wideband module, and particularly aims at ranging noise of the ultra-wideband module. The ranging error is calculated in real time and effectively compensated, so that the precision and robustness of the system are improved; through the calibration scheme of automatic calculation, the calibration efficiency is greatly improved, the operation difficulty is reduced, high-precision and high-robustness calibration can be realized, and the method is suitable for different environments and application scenes. Particularly in a multi-sensor fusion system, the method can effectively improve the positioning precision and the system stability, and meets the strict requirements for high-precision positioning and navigation.

Description

Background Art

[0002] In the fields of autonomous driving, robot navigation, indoor positioning, etc., multi-sensor fusion technology has become an important means to achieve high-precision positioning and environmental perception. Among them, lidar and ultra-wideband modules are widely adopted due to their respective advantages.

[0003] Lidar: Scans the surrounding environment using laser pulses, can achieve millimeter-level distance measurement, has high spatial resolution and environmental mapping ability, and is an important sensor for realizing high-precision obstacle detection and map construction.

[0004] Ultra-wideband module (Ultra-Wideband, UWB): Relying on its wide-band characteristics and high time resolution, it can achieve high-precision ranging within a short distance, and has strong anti-interference ability, suitable for positioning and tracking in complex environments. Although the UWB module has high accuracy in ranging, its measurement data is usually interfered by factors such as multipath effects, signal blocking, and environmental changes, resulting in large noise. This ranging noise usually needs to rely on a large amount of data statistics to estimate the noise characteristics, so as to compensate the measurement results.

[0005] LiDAR and UWB modules are commonly used combinations in multi-sensor fusion, and they are of great significance in applications such as autonomous driving, robot positioning, and indoor navigation. In a multi-sensor fusion system, fusing the data of LiDAR and UWB modules can give play to their respective advantages: using LiDAR to achieve high-precision environmental perception, and at the same time using the UWB module to achieve accurate distance measurement. However, to achieve efficient data fusion, two key technical problems must be solved first: 1. Lidar-ultra-wideband module spatial calibration: That is, how to accurately align the LiDAR and UWB modules in the same coordinate system and determine their relative positions. The spatial calibration of LiDAR and UWB modules directly affects the accuracy, robustness, and reliability of the multi-sensor system. Therefore, it is crucial to perform accurate spatial calibration.

[0006] 2. Ultra-wideband module measurement noise model calibration: The UWB module has high accuracy in ranging, but its measurement data is usually interfered by factors such as multipath effects, signal blocking, and environmental changes, resulting in large noise. Therefore, in order to improve the robustness and accuracy of multi-sensor fusion, it is necessary to calibrate the noise model of the UWB module and compensate the results through the noise model.

[0007] However, the spatial calibration of LiDAR and UWB modules faces many challenges, especially in environments with high-precision and high-robustness requirements. Most traditional calibration methods rely on manual operations for manual calibration, which is not only time-consuming and laborious, but also often results in insufficient calibration accuracy due to human errors and is difficult to meet the real-time and stability requirements of calibration in dynamic environments. In addition, the ranging data of UWB modules is usually interfered by various noise sources, and existing technologies often rely on a large amount of experimental data statistics to obtain the characteristics of ranging noise. This process is complex and difficult to ensure high precision, and cannot adapt to changes in different environments.

[0008] Existing solutions: 1. Spatial calibration of LiDAR-UWB modules: Currently, the existing spatial calibration solutions mainly adopt manual calibration methods, using laser rangefinders, total stations or other precision measurement tools to directly measure the three-dimensional translation matrix from the UWB module coordinate system to the LiDAR coordinate system. This requires calibrating fixed reference objects in the actual environment and manually determining the relative positions and postures of the two.

[0009] 2. Calibration scheme for the measurement noise model of UWB modules: Currently, the existing noise model calibration solutions mainly adopt the big data statistics method. Multiple UWB module ranging measurements are carried out in a preset environment, and a large amount of data is collected to statistically analyze the measurement errors. It is usually assumed that the noise follows a certain statistical distribution (such as Gaussian distribution), and the mean and variance of the noise are calculated. And in the way of multi-source fusion, image measurement data and UWB ranging data are collected. This calibration method is not applicable to the LiDAR-UWB module system lacking camera sensors.

[0010] Disadvantages of existing solutions: 1. Spatial calibration of LiDAR-UWB modules: (1) Complicated manual operation: The existing spatial calibration of LiDAR-UWB modules mainly relies on manual measurement using professional tools. By directly measuring the three-dimensional translation matrix from the UWB module coordinate system to the LiDAR coordinate system, this method is complex in operation and requires a large amount of manpower and time.

[0011] (2) Limited accuracy: Since the calibration process highly depends on manual measurement and the experience of operators, it is easily affected by human errors and the accuracy limitations of tools, and it is difficult to ensure continuous high-precision calibration effects in dynamic or complex environments.

[0012] 2. Calibration of the measurement noise model of UWB modules: (1) Relying on a large amount of data statistics: Existing technologies usually need to collect a large amount of UWB module ranging data for statistical analysis to estimate the distribution characteristics of noise (such as mean and variance). This method takes a long time and has a large dependence on the amount of data.

[0013] (2) Lack of real-time performance and adaptability: In the case of dynamic environmental changes, the pre-statistical noise model often fails to reflect the current actual measurement conditions in a timely manner, resulting in unsatisfactory noise compensation effects in practical applications, affecting the accuracy of data fusion and the robustness of the system. SUMMARY OF THE INVENTION

[0014] The technical problem to be solved by the present invention is to provide a joint calibration method and system for solving the technical problems of poor accuracy and high complexity in traditional manual calibration by combining the high-precision environmental perception ability of lidar and the high-precision distance measurement of the ultra-wideband module, effectively improving the positioning accuracy and system stability, and meeting the strict requirements for high-precision positioning and navigation.

[0015] The present invention adopts the following technical solutions: In a first aspect, a joint calibration method is provided, including the following steps: Acquisition A set of valid lidar point clouds and data of ranging ultra-wideband anchors; According to the obtained A set of valid lidar point clouds and data of ranging ultra-wideband anchors, establish a mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor in the lidar coordinate system and the ranging information of the ultra-wideband system; According to the mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor in the lidar coordinate system and the ranging information of the ultra-wideband system, construct a loss function model for solving the translational offset and the standard deviation of the ultra-wideband ranging noise; Use an optimization algorithm to solve the obtained optimization problem, and complete the joint calibration based on the obtained optimal parameters ( , σ*), is the translation matrix, and σ is the variance of the noise.

[0016] Preferably, the acquisition of A set of valid lidar point clouds and data of ranging ultra-wideband anchors is specifically: Arrange an ultra-wideband anchor, paste a reflective sticker at the ultra-wideband anchor, then rigidly connect the lidar and the ultra-wideband tag, control the lidar and the ultra-wideband tag to move in a figure-eight shape, and acquire N sets of valid lidar point clouds and data of ranging ultra-wideband anchors.

[0017] Preferably, record the coordinates of the ultra-wideband anchor detected by the lidar at each moment; calculate the center coordinate of each frame of ultra-wideband anchor in the lidar coordinate system , and at the same time record the distance measured by the ultra-wideband system from the ultra-wideband tag to the ultra-wideband anchor; obtain sets of valid data, ≥ 10.

[0018] Preferably, the central coordinates of each frame of ultra-wideband anchor points are as follows:

[0019] wherein, is the coordinate of the th ultra-wideband anchor point detected in the i-th frame, .

[0020] Preferably, the position of the ultra-wideband anchor point in the lidar coordinate system is determined by light intensity screening, and then the ultra-wideband tag communicates with the ultra-wideband anchor point to calculate the straight-line distance between the ultra-wideband tag and the ultra-wideband anchor point in real time, and obtain the point cloud information scanned by the lidar and the ranging information of the ultra-wideband tag.

[0021] Preferably, a mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor point in the lidar coordinate system and the ranging information of the ultra-wideband system is established as follows: Let the position of the ultra-wideband anchor point in the lidar coordinate system be , and after passing through the translation matrix , the position in the ultra-wideband coordinate system is obtained ; determine the ranging value of the ultra-wideband system.

[0022] Preferably, the ranging value of the ultra-wideband system is as follows:

[0023] wherein, , is the standard deviation of the ultra-wideband ranging noise.

[0024] Preferably, the least squares method is used to solve the loss function model of the translation matrix and the standard deviation of the ultra-wideband ranging noise, the objective function is defined, and the optimal t* is solved by the Levenberg–Marquardt or other non-linear least squares algorithms to make the smallest, and the objective function is as follows:

[0025] wherein, is the position of the ultra-wideband anchor point in the lidar coordinate system, is the ranging value of the ultra-wideband system.

[0026] Preferably, the joint maximum likelihood estimation is used to solve the loss function model of the translation offset and the standard deviation of the ultra-wideband ranging noise, and the translation offset t and the ultra-wideband ranging noise standard are jointly optimized to obtain the optimal parameters , and the loss function is as follows:

[0027] Among them, is the variance of the ultra-wideband ranging noise, is the position of the ultra-wideband anchor point in the lidar coordinate system, is the ranging value of the ultra-wideband system.

[0028] In a second aspect, an embodiment of the present invention provides a joint calibration device, including: An acquisition module that acquires data of a group of valid laser point clouds and ranging ultra-wideband anchor points; A construction module that, based on the obtained data of a group of valid laser point clouds and ranging ultra-wideband anchor points, establishes a mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor point in the lidar coordinate system and the ranging information of the ultra-wideband system; A solution module that constructs a loss function model for solving the translation matrix and the standard deviation of the ultra-wideband ranging noise according to the mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor point in the lidar coordinate system and the ranging information of the ultra-wideband system; A calibration module that uses an optimization algorithm to solve the obtained optimization problem and completes the joint calibration based on the obtained optimal parameters ( , σ*), is the translation matrix, and σ is the variance of the noise.

[0029] In a third aspect, an electronic device is provided. The electronic device includes a processor and a memory, and a computer instruction is stored on the memory. When the computer instruction is executed by the processor, the electronic device performs the actions of the method according to the first aspect or any one of its embodiments above.

[0030] In a fourth aspect, a computing device cluster is provided. The computing device cluster includes at least one computing device, and each computing device includes a processor and a memory; the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device so that the computing device cluster performs the operations of the method according to the first aspect or any one of its embodiments above.

[0031] In a fifth aspect, a computer-readable storage medium is provided. A computer-executable instruction is stored on the computer-readable storage medium, and when the computer-executable instruction is executed by a processor, the operations of the method according to the first aspect or any one of its embodiments above are implemented.

[0032] In a sixth aspect, a computer program or a computer program product is provided. The computer program or the computer program product is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, implement the operations of the method according to the first aspect or any of its embodiments described above.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects: A joint calibration method realizes the spatial calibration between a lidar and an ultra-wideband module and the calibration of the ranging noise model of the ultra-wideband module through an automated method; it automatically determines the position of the ultra-wideband anchor A in the lidar coordinate system by using the high-intensity reflection characteristics of the reflective patch in the lidar point cloud, avoiding the cumbersome operations and human errors in the traditional manual calibration process; by collecting the data of the lidar and the ultra-wideband module in real time, it automatically calibrates the ranging noise model of the ultra-wideband module, effectively improving the overall accuracy and robustness of the multi-sensor system. This not only reduces the system debugging and maintenance costs, but also improves the calibration efficiency.

[0034] Furthermore, the lidar and the UWB tag are fixed on the same rigid structure to ensure that their relative positions remain unchanged all the time, avoiding the coordinate offset error caused by the equipment shaking during movement, and providing a stable spatial relationship basis for subsequent calibration. Controlling the device to move along an "8"-shaped trajectory can cover multi-directional movements such as front and back, left and right, and rotation at the same time, collecting data at different distances and angles, and avoiding the one-sidedness of the calibration results caused by data in a single direction; environmental interferences (such as obstacle occlusion and signal reflection) are naturally introduced during the dynamic movement, enabling the calibration results to directly reflect the noise characteristics in the real scene and enhancing the robustness of subsequent practical applications.

[0035] Furthermore, each anchor records multiple frames of coordinates (such as 30 frames per second), and by calculating the center coordinates , the accidental errors (such as point cloud noise) of a single scan of the lidar can be effectively eliminated, making the anchor positioning more accurate. At the same time, the LiDAR coordinates and the UWB ranging are recorded to ensure strict spatio-temporal alignment and avoid the coordinate-distance matching deviation caused by equipment delay. ≥10 groups of data covering different poses (such as far and near, angle changes), and by using the geometric relationships of multiple groups of distance-coordinates for cross-verification, abnormal data (such as UWB signal jumps) can be automatically eliminated, improving the calibration reliability.

[0036] Furthermore, the anchor is automatically identified through the light intensity data (such as high-reflection marks) of the lidar, which is more accurate than manual marking, and the error can be controlled within the centimeter level, avoiding visual deviation; the UWB real-time ranging is synchronized with the lidar scan to ensure the strict correspondence between the distance and the coordinate at each pose, solving the "time drift" problem of data asynchronization in the traditional method.

[0037] Furthermore, the least squares method directly models the geometric error and minimizes the overall deviation through optimization, with the accuracy improved by more than 30% compared to empirical estimation (such as manual averaging); the Levenberg-Marquardt algorithm automatically balances gradient descent and the Gauss-Newton method, and still converges stably when there are jump noises in UWB ranging, avoiding result divergence; simultaneously solving the translation amount t and the noise standard deviation σ, it solves coordinate alignment and error modeling at one time, saving 50% of the calculation time compared to traditional step-by-step calibration.

[0038] Furthermore, it calculates the translation offset and noise parameters simultaneously, avoiding the vicious cycle of error interaction in step-by-step calibration. The overall accuracy is improved by more than 40% compared to the traditional step-by-step method. It automatically fits the true noise distribution of UWB ranging through maximum likelihood estimation without presetting a noise model, and the measured noise estimation error is <5% in a complex environment; the probabilistic modeling can automatically reduce the weight of incorrect ranging data (such as UWB signal mutations), and even if 20% of the data is abnormal, the calibration result remains stable.

[0039] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0040] In summary, the method of the present invention ensures device synchronization through rigid connection, improves the comprehensiveness of calibration by collecting multi-view data through figure-eight motion, jointly optimizes the algorithm to synchronously calculate the translation offset and noise parameters, avoids error transmission, and achieves centimeter-level accuracy; the adaptive noise reduction model can resist the interference of abnormal data, and the full-process automation improves the calibration efficiency, quickly completing highly robust calibration in a dynamic environment.

[0041] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the following described drawings are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0043] Figure 1 Shows the flow schematic diagram of the present invention; Figure 2 Shows the data acquisition schematic diagram of the present invention; Figure 3 Shows a schematic block diagram of an example device that can be used to implement the embodiments of the present disclosure; Figure 4A schematic block diagram of an example computing device cluster that can be used to implement embodiments of the present disclosure is shown; Figure 5 A schematic block diagram of an example implementation of a computing device cluster that can be used to implement embodiments of the present disclosure is shown.

[0044] Wherein, 900. Computing device; 900A. Computing device A; 900B. Computing device B; 902. Bus; 904. Processor; 906. Memory; 908. Communication interface; 1000. Computing device cluster; 1100. Implementation; 1110. Network. Detailed implementation manners

[0045] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the protection scope of the present application.

[0046] In the description of the present invention, it should be understood that the terms "including" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0047] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0048] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations. For example, A and / or B can represent: the case where A exists alone, the case where A and B exist simultaneously, and the case where B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the preceding and following related objects.

[0049] It should be understood that although terms such as first, second, and third may be used in the embodiments of the present invention to describe preset ranges and the like, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0050] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0051] Schematic diagrams of various structures according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0052] The present invention provides a joint calibration method. Through the fusion of lidar and ultra-wideband module data, the spatial calibration parameters of the device are automatically calculated and optimized. Specifically for the ranging noise of the ultra-wideband module, the present invention uses an accurate optimization algorithm to calculate the ranging error in real time and perform effective compensation, thereby improving the accuracy and robustness of the system; through the automatically solved calibration scheme, not only the calibration efficiency is greatly improved, the operation difficulty is reduced, but also high-precision and high-robustness calibration can be achieved, adapting to different environments and application scenarios. Especially in a multi-sensor fusion system, the method of the present invention can effectively improve the positioning accuracy and system stability, meeting the strict requirements for high-precision positioning and navigation.

[0053] Embodiment 1 Please refer to Figure 1 , a joint calibration method of the present invention includes the following steps: S1. Equipment preparation: Rigidly connect the lidar and the ultra-wideband tag, and arrange the ultra-wideband anchor points in the experimental environment; The specific equipment includes: 1 lidar, 2 ultra-wideband modules, and 1 airborne terminal.

[0054] S101. Rigidly connect a lidar with an ultra-wideband module (hereinafter referred to as ultra-wideband tag T), and the parameter to be calibrated is the ultra-wideband; the translation matrix from the origin of tag T to the lidar coordinate system , and connect both to the airborne terminal; S102. Place another ultra-wideband module in the experimental environment (hereinafter referred to as ultra-wideband anchor A), and stick a reflective patch on its signal emission area. Since the light intensity of the reflective patch is significantly higher than that of the surrounding environmental objects in the point cloud obtained by the lidar, the position of ultra-wideband anchor A in the lidar coordinate system can be determined by light intensity screening; S103. Ultra-wideband tag T communicates with ultra-wideband anchor A, and calculates the straight-line distance between the two in real time (this distance contains the noise ε to be calibrated); S104. Obtain the point cloud information scanned by the lidar and the ranging information of ultra-wideband tag T in real time through the airborne terminal.

[0055] S2. Data acquisition: Use the combined rigid body of the handheld lidar and the ultra-wideband tag prepared in step S1 to make a figure-eight motion, and collect N sets of effective lidar point clouds and ranging ultra-wideband anchor data; Please refer to Figure 2 , and the specific steps of data acquisition are as follows: S201. The experimental equipment starts to operate, and the combined rigid body of the handheld lidar and ultra-wideband tag T makes a figure-eight motion around ultra-wideband anchor A (the figure-eight motion can make the collected coordinate data have large differences in all directions); S202. Record the coordinates of the reflective patch of ultra-wideband anchor A (i.e., ultra-wideband anchor A) detected by the lidar at each moment; there will be multiple reflective patch points in each frame, calculate the center coordinate of the reflective patch points in each frame, and denote it as (in the lidar coordinate system), and at the same time record the distance from ultra-wideband tag T to ultra-wideband anchor A measured by the ultra-wideband system, and denote it as ; a total of sets of effective data are obtained, ≥ 10; Assume that reflective patch points are detected in the i-th frame, and determine the coordinate center of the corresponding frame.

[0056] The center coordinate of the corresponding frame is expressed as:

[0057] where, is the coordinate of reflective patch points detected in the i-th frame, .

[0058] S3. Establish a mathematical model: According to the lidar point cloud and ultra-wideband ranging information, establish a mathematical model for the relationship between the three-dimensional coordinates of the ultra-wideband anchor points in the lidar coordinate system and the ranging information of the ultra-wideband system; S301. Assume that the position of the ultra-wideband anchor point A in the lidar coordinate system is , after passing through the translation matrix , its position in the ultra-wideband coordinate system is ; S302. Considering the measurement noise, determine that the ranging value of the ultra-wideband system satisfies the model conditions.

[0059] The ranging value of the ultra-wideband system is:

[0060] where, , is the standard deviation of the ultra-wideband ranging noise.

[0061] S4. Construct an optimization problem: According to the mathematical model established in step S3 for the relationship between the three-dimensional coordinates of the ultra-wideband anchor points in the lidar coordinate system and the ranging information of the ultra-wideband system, construct a loss function model for solving the translation offset and the standard deviation of the ultra-wideband ranging noise; To simultaneously solve the translation matrix and the standard deviation σ of the ultra-wideband ranging noise, the following two optimization schemes are constructed: 1) Least squares method Construct the residual:

[0062] Define the objective function:

[0063] Solve for the optimal t* to minimize through the Levenberg–Marquardt or other non-linear least squares algorithms.

[0064] 2) Joint maximum likelihood estimation (MLE) Assume that each is independent and follows , then the likelihood function of the observed data is:

[0065] Take the negative log-likelihood (ignoring the constant term) to obtain the loss function:

[0066] Jointly optimize t and σ to obtain the optimal parameters .

[0067] The goal of joint calibration is to obtain the optimal parameters ( , σ*), where t is a 4x4 translation matrix and σ is a real number representing the variance of the noise; ( , σ*) are the optimal values obtained through optimization. Obtaining ( , σ*) indicates that the calibration process has ended.

[0068] Therefore, the result of calibration is that the translation matrix is , and the measurement noise model of UWB is .

[0069] S5. Solve the optimization problem obtained in step S4 using an optimization algorithm and verify the result.

[0070] S501. Initial estimation Give an initial guess of t based on experience or prior information; alternatively, first solve for t using the least squares method and calculate the initial σ using the residuals:

[0071] S502. Nonlinear optimization Use the Levenberg–Marquardt or other optimization algorithms to iteratively optimize the joint loss function L( , σ) to obtain the optimal parameters ( , σ*).

[0072] S503. Verification Use the obtained t and σ to make predictions on new data:

[0073] Compare the predicted value with the actual ranging value to verify the calibration effect.

[0074] Those skilled in the art of the present technology can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0075] Example 2 The present invention provides a joint calibration device, which can be used to implement the above joint calibration method. Specifically, the joint calibration device includes an acquisition module, a construction module, a solution module, and a calibration module.

[0076] Among them, the acquisition module acquires data of a set of valid lidar point clouds and ranging ultra-wideband anchors; The construction module, based on the obtained data of a set of valid lidar point clouds and ranging ultra-wideband anchors, establishes a mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchors in the lidar coordinate system and the ranging information of the ultra-wideband system; The solution module constructs a loss function model for solving the translation matrix and the standard deviation of the ultra-wideband ranging noise according to the mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchors in the lidar coordinate system and the ranging information of the ultra-wideband system; The calibration module uses an optimization algorithm to solve the obtained optimization problem and completes joint calibration based on the obtained optimal parameters ( , σ*), is the translation matrix, and σ is the variance of the noise.

[0077] Embodiment 3 An embodiment of the present disclosure also provides a computing device 900. As Figure 3 shown, the computing device 900 includes: a bus 902, a processor 904, a memory 906, and a communication interface 908. The processor 904, the memory 906, and the communication interface 908 communicate with each other through the bus 902. The computing device 900 can be a server or a terminal device. It should be understood that the present application does not limit the number of processors and memories in the computing device 900.

[0078] The bus 902 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 3 only one line is shown in, but it does not mean that there is only one bus or one type of bus. The bus 904 can include a path for transmitting information between various components of the computing device 900 (for example, the memory 906, the processor 904, the communication interface 908).

[0079] The processor 904 may include any one or more of processors such as a central processing unit (CPU), a graphics processing unit (GPU), a Tensor Processing Unit (TPU), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a micro processor (MP), or a digital signal processor (DSP).

[0080] The memory 906 may include volatile memory, such as random access memory (RAM). The processor 904 may also include non-volatile memory, such as read-only memory (ROM), flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0081] The memory 906 stores executable program code, and the processor 904 executes the executable program code to implement, for example, a joint calibration method. That is, the memory 906 may store instructions for the methods and functions of the computing device involved in any of the above embodiments.

[0082] The communication interface 908 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device 900 and other devices or a communication network.

[0083] Embodiment 4 An embodiment of the present disclosure further provides a computing device cluster 1000. The computing device cluster includes at least one computing device. The computing device may be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device may also be a terminal device such as a desktop computer, a laptop computer, or a smart phone.

[0084] As Figure 4As shown, the computing device cluster includes at least one computing device 900. Instructions for executing the methods and functions of the computing device involved in any of the above embodiments may be stored in the memory 906 of one or more computing devices 900 in the computing device cluster.

[0085] In some possible implementation manners, instructions for executing the methods and functions of the computing device involved in any of the above embodiments may also be separately stored in the memory 906 of one or more computing devices 900 in the computing device cluster. In other words, a combination of one or more computing devices 900 may jointly execute the instructions for executing the methods and functions of the computing device. Specifically as follows: Collect a set of valid lidar point clouds and data of ranging ultra-wideband anchors; according to the obtained set of valid lidar point clouds and data of ranging ultra-wideband anchors, establish a mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchors in the lidar coordinate system and the ranging information of the ultra-wideband system; according to the mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchors in the lidar coordinate system and the ranging information of the ultra-wideband system, construct a loss function model for solving the translational offset and the standard deviation of the ultra-wideband ranging noise; use an optimization algorithm to solve the obtained optimization problem, and complete the joint calibration based on the obtained optimal parameters ( , σ*), where is the translation matrix and σ is the variance of the noise.

[0086] In some possible implementation manners, one or more computing devices in the computing device cluster may be connected through a network. Among them, the network may be a wide area network or a local area network, etc. Figure 5 Fig. 1100 shows a possible implementation manner. As Figure 5 shown, two computing devices 900A and 900B are connected through a network 1110. Specifically, they are connected to the network through the communication interfaces in each computing device. In this type of possible implementation manners, for example, the instructions for executing the joint calibration method are stored in the memory 906 of the computing device 900A.

[0087] It should be understood that Figure 5 the functions of the computing device 900A shown in Fig. may also be completed by multiple computing devices 900. Similarly, the functions of the computing device 900B may also be completed by multiple computing devices 900. Embodiments of the present disclosure also provide a computer program product including instructions, which when running on a computer, cause the computer to execute the methods and functions of the computing device involved in any of the above embodiments.

[0088] Embodiment 5 Embodiments of the present disclosure also provide a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods and functions of a computing device in any of the above embodiments.

[0089] In general, the various embodiments of the present disclosure may be implemented in hardware or special-purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, a microprocessor, or other computing devices. Although the various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented as, by way of non-limiting example, hardware, software, firmware, special-purpose circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0090] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the processes / methods as referred to the accompanying drawings above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules may be combined or divided as needed. The machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in local and remote storage media.

[0091] The computer program code for implementing the methods of the present disclosure may be written in one or more programming languages. These computer program codes may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when the program code is executed by the computer or other programmable data processing apparatus, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code may be executed entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0092] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier so that the device, apparatus, or processor can perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0093] A computer-readable medium can be any tangible medium that contains or stores a program for or related to an instruction execution system, apparatus, or device, or a data storage device such as a data center that contains one or more available media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0094] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0095] Supplementary application examples: Application example: Mapping of a ground mobile robot integrating lidar-IMU-UWB Task: A ground mobile robot equipped with a Livox MID-360 lidar (with an ICM40609 IMU built-in) and a Nooplooplinktrack P-B UWB performs simultaneous localization and mapping operations in a corridor environment. Results:

[0096] Monte Carlo analysis Simulation environment: Build a virtual indoor scene (20m×20m), arrange 4 UWB anchors, rigidly fix the lidar and the UWB receiver on the mobile robot, and preset the true translational offset t gt =[0.5,−0.2,0.1] T m, and the standard deviation of the UWB ranging noise σ gt =0.15m.

[0097] Comparison method: Traditional manual calibration: Manually measure the installation offset and fix σ = 0.1 m; Separate calibration method: First calibrate t (least squares), and then count σ ; This method (joint optimization).

[0098] Experimental results

[0099] Application example: Multi-robot collaborative mapping Task: 3 AGVs collaborate to build a warehouse map (Intel RealSense L515 lidar + UWB).

[0100] Results: Calibration time consumption: The calibration time of a single robot is reduced from 30 minutes in the traditional method to 3 minutes.

[0101] Map alignment accuracy: The global error of collaborative mapping is reduced from 0.35 m to 0.15 m.

[0102] Real-time performance: The online calibration update frequency reaches 10 Hz, supporting continuous calibration in a dynamic environment.

[0103] In summary, the present invention provides a joint calibration method and system, which uses the point cloud data of the lidar and the ranging data of the ultra-wideband module collected in real time to establish and update the three-dimensional translation matrix of the lidar-ultra-wideband module spatial calibration and the ultra-wideband module measurement noise model, introduces automatic solution and data fusion technology, gets rid of the cumbersome, inefficient and manual-experience-dependent problems in traditional manual calibration, realizes the automatic alignment between the lidar coordinate system and the ultra-wideband module coordinate system and the estimation of the ultra-wideband module measurement noise model, thereby significantly improving the accuracy of the lidar-ultra-wideband module fusion system and the overall robustness of the system.

[0104] In addition, although the operations of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the shown operations must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart may be changed in the order of execution. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution. It should also be noted that the features and functions of two or more devices according to the present disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0105] The various implementations of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The choice of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the art to understand the various implementation manners disclosed herein.

Claims

1. A joint calibration method, characterized in that: The following steps are involved: collection A set of valid laser point cloud and ranging ultra-wideband anchor point data; According to the obtained Build effective laser point cloud and ranging UWB anchor point data, and establish a mathematical model of the relationship between the three-dimensional coordinates of the UWB anchor point in the laser radar coordinate system and the ranging information of the UWB system; Based on the mathematical model of the relationship between the three-dimensional coordinates of the UWB anchor point in the laser radar coordinate system and the UWB system ranging information, a loss function model for solving the translation offset and the standard deviation of the UWB ranging noise is constructed. The optimization problem is solved using the optimization algorithm, based on the obtained optimal parameters ( ,σ*) completes the joint calibration, is the translation matrix, and σ is the variance of the noise.

2. The joint calibration method according to claim 1, characterized in that: collection A set of valid laser point cloud and ranging ultra-wideband anchor point data, specifically: Arrange an ultra-wideband anchor point and paste a reflective sticker on the ultra-wideband anchor point, then rigidly connect the lidar and the ultra-wideband tag, control the lidar and the ultra-wideband tag to move in a figure eight, and collect N groups of valid laser point clouds and ranging ultra-wideband anchor point data.

3. The joint calibration method according to claim 2, characterized in that: Record the coordinates of the ultra-wideband anchor point detected by the lidar at each moment; calculate the center coordinates of the ultra-wideband anchor point in each frame in the lidar coordinate system , and record the distance from the UWB tag to the UWB anchor point measured by the UWB system ; Get Group valid data, ≥10.

4. The joint calibration method according to claim 3, characterized in that: The center coordinates of the UWB anchor point in each frame for: in, is detected in the i-th frame The coordinates of the UWB anchor points, .

5. The joint calibration method according to claim 3, characterized in that: The position of the UWB anchor point in the LiDAR coordinate system is determined by light intensity screening, and then the UWB tag communicates with the UWB anchor point, and the straight-line distance between the UWB tag and the UWB anchor point is calculated in real time to obtain the point cloud information scanned by the LiDAR and the ranging information of the UWB tag.

6. The joint calibration method according to claim 1, characterized in that: The mathematical model of the relationship between the three-dimensional coordinates of the ultra-wideband anchor point in the laser radar coordinate system and the ranging information of the ultra-wideband system is established as follows: Assume the position of the ultra-wideband anchor point in the lidar coordinate system is , after translation matrix Then, we get the position in the UWB coordinate system. ; Determine the ranging value of the ultra-wideband system .

7. The joint calibration method according to claim 6, characterized in that: Ultra-wideband system ranging value for: in, , is the standard deviation of ultra-wideband ranging noise.

8. The joint calibration method according to claim 1, characterized in that: The least squares method is used to solve the loss function model of the translation matrix and the standard deviation of the ultra-wideband ranging noise, define the objective function, and use the Levenberg–Marquardt or other nonlinear least squares algorithm to solve the optimal t*. Minimum, the objective function is as follows: in, is the position of the ultra-wideband anchor point in the lidar coordinate system, It is the ranging value of the ultra-wideband system.

9. The joint calibration method according to claim 1, characterized in that: The joint maximum likelihood estimation is used to solve the loss function model of translation offset and ultra-wideband ranging noise standard deviation, and the translation offset t and ultra-wideband ranging noise standard deviation are jointly optimized. , get the optimal parameters , the loss function is as follows: in, is the UWB ranging noise variance, is the position of the ultra-wideband anchor point in the lidar coordinate system, It is the ranging value of the ultra-wideband system.

10. A combined calibration device, characterized in that: include: Acquisition module, acquisition A set of valid laser point cloud and ranging ultra-wideband anchor point data; Building modules, based on the obtained Build effective laser point cloud and ranging UWB anchor point data, and establish a mathematical model of the relationship between the three-dimensional coordinates of the UWB anchor point in the laser radar coordinate system and the ranging information of the UWB system; The solution module constructs a loss function model for solving the translation matrix and the standard deviation of the UWB ranging noise based on the mathematical model of the relationship between the three-dimensional coordinates of the UWB anchor point in the laser radar coordinate system and the ranging information of the UWB system; The calibration module uses the optimization algorithm to solve the obtained optimization problem, based on the obtained optimal parameters ( ,σ*) completes the joint calibration, is the translation matrix, and σ is the variance of the noise.

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