Calibration methods, devices, equipment, media, and products for SLAM measurement system base control points

Abstract

This invention relates to the field of mobile mapping technology, and particularly to a method, apparatus, equipment, medium, and product for calibrating base control points of a SLAM measurement system. This application involves setting up a target at the calibration site and obtaining its center's high-precision reference coordinates in a global coordinate system; then physically aligning the SLAM system's base control points with the target center and acquiring point cloud data in a static state; next, registering the target's reference coordinates with corresponding features in the SLAM point cloud to solve for the transformation relationship between the two coordinate systems; finally, calculating the precise calibration coordinates of the base control points in the SLAM system coordinate system based on this transformation relationship. This transforms the SLAM system's scanning of the calibration environment into a static coordinate transformation solution, correlating the calibration accuracy of the base control points with the high-precision global measurement results, and providing a reliable coordinate traceability starting point for the SLAM measurement system.

Description

Technical Field

[0001] This invention relates to the field of mobile surveying technology, and in particular to methods, apparatus, equipment, media and products for calibrating base control points of SLAM surveying systems. Background Technology

[0002] With the continuous development of 3D spatial information acquisition technology, simultaneous localization and mapping (SLAM) technology plays an increasingly important role in various fields requiring autonomous movement and environmental awareness. This technology enables mobile platforms to build models of their surroundings in real time while simultaneously determining their own position in unknown environments, laying a solid foundation for automated and intelligent operations.

[0003] In practical applications, to facilitate the conversion of locally acquired data to a unified global coordinate system, a specific physical reference point, known as a base control point, is typically set on the mechanical structure of the measurement system. The coordinates of this control point are considered the spatial reference for the system's position throughout the measurement task. Ideally, as long as the relationship between this control point and the system's internal sensor coordinate system is accurately calibrated, data fusion can be achieved through simple coordinate transformations. However, the calibration of such base control points currently often relies on the system's own pose estimation accuracy. Because the system inevitably accumulates errors during movement, there is uncertainty in its estimation of the position of its own coordinate system origin (i.e., the base control point) in the global coordinate system. This inaccuracy is directly transmitted to all subsequent measurement data, thereby reducing the reliability of multi-source data fusion and the accuracy of the overall model.

[0004] Therefore, how to achieve high-precision calibration of the base control points in the system's internal coordinate system without relying on pose estimation that may have errors in the system itself, so as to ensure the accuracy of all subsequent spatial data conversions from the source, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, medium, and product for calibrating the base control points of a SLAM measurement system. The aim is to solve the technical problem in the prior art of how to achieve high-precision calibration of the base control points in the system's internal coordinate system without relying on pose estimation that may have errors in the system itself, so as to ensure the accuracy of all subsequent spatial data conversions from the source.

[0006] To achieve the above objectives, the present invention provides a method for calibrating control points on the base of a SLAM measurement system, the method comprising the following steps: Targets are set up at the calibration site, and the reference coordinates of the target center point in the global coordinate system are obtained. The base control point of the SLAM measurement system is physically aligned with the center point of the target, and SLAM point cloud data is collected in a static state after alignment. Based on the reference information of the global coordinate system, data registration is performed with the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system; Based on the transformation relationship and the reference coordinates of the target center point, the calibration coordinates of the base control point in the SLAM system coordinate system are obtained.

[0007] Optionally, the data registration based on the reference information of the global coordinate system and the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system includes: The SLAM point cloud data acquired during the static scanning phase is used as the point cloud to be registered, and the coordinate system of the SLAM system itself when acquiring the SLAM point cloud data is defined as the SLAM system coordinate system. Obtain the coordinates of at least three target center points in the global coordinate system, which were previously measured by a 3D laser scanner, and use the coordinates as reference information; Using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target, the transformation relationship between the SLAM system coordinate system and the global coordinate system is calculated.

[0008] Optionally, the target is a black and white checkerboard planar target; The step of using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target includes: Identify the local point cloud region in the SLAM point cloud data that corresponds to the black and white checkerboard plane target; Based on the geometric features of the local point cloud region, the equivalent coordinates of the target center point in the SLAM system coordinate system are calculated, and the point cloud features of the equivalent coordinates are used as the registration target.

[0009] Optionally, the step of setting up a target at the calibration site and obtaining the reference coordinates of the target's center point in the global coordinate system includes: The black and white checkerboard-patterned planar target is deployed within the calibration area. A high-precision terrestrial 3D laser scanner was used to scan the calibration site containing the target to obtain high-precision point cloud data. The center point coordinates of the black and white checkerboard planar target are identified and extracted from the high-precision point cloud data and used as the reference coordinates.

[0010] Optionally, calculating the transformation relationship between the SLAM system coordinate system and the global coordinate system includes: The equivalent coordinates of the target center point in the SLAM system coordinate system are taken as the source point set, and the reference coordinates in the global coordinate system are taken as the target point set. The source point set and the target point set are used as inputs, and the ICP algorithm strategy is applied to iteratively solve the transformation parameters until the preset iteration termination condition is reached. The transformation parameters include rotation matrix and translation vector. Based on the transformation parameters at the termination of the iteration, construct the transformation matrix from the SLAM system coordinate system to the global coordinate system: The transformation matrix is ​​used as the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0011] Optionally, after physically aligning the base control point of the SLAM measurement system with the center point of the target, and acquiring SLAM point cloud data in a stationary state after alignment, the method further includes: Calculate the spatial residual between the coordinates of the registered target center point in the SLAM system coordinate system and the reference coordinates; If the magnitude of the spatial residual is less than or equal to a preset threshold, the calibration result is determined to be valid and the calibration coordinates are output. If the magnitude of the spatial residual is greater than a preset threshold, the step of physically aligning the base control point of the SLAM measurement system with the center point of the target is repeated.

[0012] Furthermore, to achieve the above objectives, the present invention also proposes a calibration device for the base control points of a SLAM measurement system, the calibration device comprising: The data acquisition module is used to set up targets at the calibration site and obtain the reference coordinates of the target center point in the global coordinate system. The data acquisition module is also used to physically align the base control point of the SLAM measurement system with the center point of the target, and to acquire SLAM point cloud data in a static state after alignment. The coordinate transformation calculation module is used to perform data registration with the SLAM point cloud data based on the reference information of the global coordinate system, so as to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system. The coordinate transformation calculation module is also used to obtain the calibration coordinates of the base control point in the SLAM system coordinate system based on the transformation relationship and the reference coordinates of the target center point.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a calibration device for the base control points of a SLAM measurement system. The calibration device for the base control points of a SLAM measurement system includes: a memory, a processor, and a calibration program for the base control points of a SLAM measurement system stored in the memory and executable on the processor. The calibration program for the base control points of a SLAM measurement system is configured to implement the steps of the calibration method for the base control points of a SLAM measurement system as described above.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a calibration program for the base control points of a SLAM measurement system. When the calibration program for the base control points of a SLAM measurement system is executed by a processor, it implements the steps of the calibration method for the base control points of a SLAM measurement system as described above.

[0015] The present application proposes one or more technical solutions that have at least the following technical effects: By introducing a high-precision global coordinate system as a benchmark and utilizing easily identifiable targets as common reference objects, the present invention transforms the scanning of the calibration environment by the SLAM system into a static coordinate transformation solution. This method avoids the excessive reliance on the pose estimation accuracy of the SLAM system itself in traditional approaches, directly linking the calibration accuracy of the base control points with the high-precision global measurement results. Therefore, it effectively improves the accuracy and reliability of the calibration results, and the operation process is clear, providing a stable and reliable coordinate traceability starting point for the SLAM measurement system. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the first embodiment of the calibration method for the base control points of the SLAM measurement system of the present invention. Figure 2 This is a flowchart illustrating the second embodiment of the calibration method for the base control points of the SLAM measurement system of the present invention; Figure 3 This is a structural block diagram of the first embodiment of the calibration device for the base control point of the SLAM measurement system of the present invention; Figure 4This is a schematic diagram of the structure of the calibration device for the base control point of the SLAM measurement system in the hardware operating environment involved in the embodiments of the present invention.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is as follows: a target is set up at the calibration site, and the reference coordinates of the target center point in the global coordinate system are obtained; the base control point of the SLAM measurement system is physically aligned with the target center point, and SLAM point cloud data is collected in the static state after alignment; data registration is performed based on the reference information of the global coordinate system and the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system; according to the transformation relationship and the reference coordinates of the target center point, the calibration coordinates of the base control point in the SLAM system coordinate system are obtained.

[0023] Currently, in practical applications, to facilitate the conversion of locally acquired data to a unified global coordinate system, a specific physical reference point, namely the base control point, is typically set on the mechanical structure of the measurement system. The coordinates of this control point are considered the spatial position reference of the system throughout the entire measurement task. Ideally, as long as the relationship between this control point and the system's internal sensor coordinate system is accurately calibrated, data fusion can be achieved through simple coordinate transformation. However, the calibration of such base control points currently often relies on the system's own pose estimation accuracy. Since the system inevitably accumulates errors during movement, there is uncertainty in its estimation of the position of its own coordinate system origin (i.e., the base control point) in the global coordinate system. This inaccuracy is directly transmitted to all subsequent measurement data, thereby reducing the reliability of multi-source data fusion and the accuracy of the overall model. Therefore, how to achieve high-precision calibration of the base control point in the system's internal coordinate system without relying on the pose estimation, which may contain errors within the system itself, to ensure the accuracy of all subsequent spatial data conversions from the source, is a technical problem urgently needing to be solved in this field.

[0024] It should be noted that the executing entity of this invention can be a calibration device for the control points of a SLAM measurement system base, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device for a SLAM measurement system base control point calibration device capable of performing the above functions. This embodiment does not specifically limit this. The following uses the calibration device for the control points of a SLAM measurement system base as an example to describe this embodiment and the following embodiments.

[0025] Based on this, embodiments of this application provide a calibration method for the base control points of a SLAM measurement system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the calibration method for the base control points of the SLAM measurement system of this application.

[0026] In this embodiment, the calibration method for the base control points of the SLAM measurement system includes steps S10 to S40: Step S10: Set up the target at the calibration site and obtain the reference coordinates of the target center point in the global coordinate system.

[0027] It should be noted that SLAM (Simultaneous Localization and Mapping) systems refer to mobile surveying devices that integrate multiple sensors, algorithms, and hardware platforms. Their core capability is the ability to build a real-time map of the surrounding environment in an unknown environment without relying on external pre-set beacons, while simultaneously determining their own precise location within that map. In practical applications, this typically manifests as a mobile scanning platform integrating sensors such as LiDAR, inertial measurement units, and visual cameras, such as handheld, backpack, or vehicle-mounted 3D scanners. This embodiment uses a handheld laser SLAM surveying system (hereinafter referred to as the SLAM surveying system) as an example. As a common surveying equipment, SLAM surveying systems typically have a control point designed on their base to allow for the conversion of surveying results to a geodetic coordinate system or the local coordinate system. During data acquisition, the base's control point needs to be aligned with the ground control point, and the acquisition timestamp needs to be recorded. In the post-processing stage, the point cloud acquired by the SLAM surveying system can be converted to the target coordinate system based on three or more acquisition timestamps and the corresponding ground control point coordinates.

[0028] Understandably, the calibration site refers to the physical space where calibration operations are performed. This site should have a stable surface and a relatively open field of vision to reduce the adverse effects of environmental vibrations and obstructions on the measurement. Here, the target specifically refers to a physical marker with a regular geometric shape and high contrast, such as a black and white checkerboard planar target. Its function is to serve as a common feature that is easily identifiable and accurately extracted during subsequent data registration. A global coordinate system, on the other hand, is an official coordinate system defined and maintained by the national or regional surveying and mapping authority. Examples include the national geodetic coordinate system based on satellite positioning systems, or a city-specific coordinate system used in urban planning and construction. These coordinate systems are characterized by strict definitions, unified parameters, and high-precision control points, ensuring the integration of spatial data across different projects, periods, and systems.

[0029] In a preferred embodiment, the target is a black and white checkerboard planar target, and the step of setting up the target in the calibration site and obtaining the reference coordinates of the target center point in the global coordinate system includes steps A10 to A30. Step A10: Deploy the black and white checkerboard planar target within the calibration area.

[0030] Understandably, the black and white checkerboard planar target is chosen because its corner features have extremely high recognition and positioning accuracy in both images and point cloud data. When deploying the target, it should be ensured that the target plane is stable and oriented in a direction that is conducive to subsequent data acquisition by the scanner.

[0031] Step A20: Use a high-precision terrestrial 3D laser scanner to scan the calibration site containing the target to obtain high-precision point cloud data.

[0032] It should be noted that the high-precision terrestrial 3D laser scanner is commonly referred to as a ground station in the field, and its function is to establish a high-precision spatial reference framework in the initial stage of calibration. Specifically, the ground station first scans the entire indoor environment where fixed targets are deployed, obtaining high-precision point cloud data containing target details. By processing this point cloud, the 3D coordinates of the target's center point in the global coordinate system defined within the point cloud can be accurately extracted.

[0033] It is understood that the calibration site mentioned in this embodiment includes, but is not limited to, indoor or outdoor sites. The requirements for outdoor sites are that there are at least two non-parallel building walls within 10 to 20 meters around the equipment, and the ground can be scanned by the ground station and SLAM measurement system. The scanning process needs to cover the entire target and its surrounding environment to obtain complete three-dimensional information.

[0034] Step A30: Identify and extract the center point coordinates of the black and white checkerboard planar target from the high-precision point cloud data, and use them as the reference coordinates.

[0035] It should be noted that due to the regular geometric pattern and high contrast of the checkerboard, its corner points exhibit a specific spatial distribution pattern in the point cloud. Based on this feature, the clear outline and corner positions of the black and white checkerboard target can be automatically or semi-automatically located in the acquired high-precision point cloud. The process of extracting the center point typically involves first accurately identifying the coordinates of multiple corner points of the checkerboard, and then calculating them using mathematical methods such as fitting the geometric center of the plane or the intersection of the diagonals. This method effectively eliminates single-point measurement errors, thereby obtaining more robust and accurate reference coordinates.

[0036] Step S20: Physically align the base control point of the SLAM measurement system with the center point of the target, and collect SLAM point cloud data in a static state after alignment.

[0037] It should be noted that the base control point refers to a specific mechanical structural point on the equipment base used for alignment with the ground control point. Physical alignment means manually aligning this point precisely with the target center point, whose global coordinates are known. The point cloud acquired under these static conditions uses the equipment coordinate system of the SLAM system. This frame of point cloud will serve as a reference for subsequent point cloud stitching, allowing the high-precision reference point cloud to be aligned with it through coordinate transformation.

[0038] Step S30: Based on the reference information of the global coordinate system, perform data registration with the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0039] It should be noted that data registration here refers to the process of finding optimal spatial transformation parameters using a specific point cloud matching algorithm. This transformation can optimally align the point cloud data in the SLAM device's coordinate system to a high-precision reference point cloud in the global coordinate system established by the ground station. For example, the commonly used iterative nearest-point algorithm minimizes the distance between corresponding points in two point clouds by continuously adjusting rotation and translation parameters, eventually converging to obtain a stable transformation matrix.

[0040] Understandably, the high-precision reference point cloud contains the target's precise coordinates in the global coordinate system, while the stationary SLAM point cloud records the environmental data in its own coordinate system when the device is centered on the target. Data registration leverages the geometric consistency of the same physical scene described by the two point clouds to deduce the SLAM device's pose relative to the global coordinate system at the moment of acquisition. This method utilizes common features of the entire surrounding environment, including the target, for matching, thereby improving the robustness and accuracy of the calculated transformation relationship.

[0041] In one embodiment, after physically aligning the base control point of the SLAM measurement system with the target center point and acquiring SLAM point cloud data in a stationary state after alignment, the method further includes: calculating the spatial residual between the coordinates of the registered target center point in the SLAM system coordinate system and the reference coordinates; if the magnitude of the spatial residual is less than or equal to a preset threshold, the calibration result is determined to be valid and the calibration coordinates are output; if the magnitude of the spatial residual is greater than the preset threshold, the step of physically aligning the base control point of the SLAM measurement system with the target center point is re-executed.

[0042] It should be noted that after solving the coordinate transformation relationship, this embodiment further introduces a verification step for quality assessment and control of the calibration results. Spatial residual is a key indicator for quantifying calibration accuracy. Specifically, it refers to the vector obtained by subtracting the coordinates of the target center point in the SLAM coordinate system (obtained after registration) from the reference coordinates (which serve as the true value). The magnitude of this vector, i.e., the size of its spatial distance, directly reflects the overall error level of this calibration process. The preset threshold is a pre-set accuracy tolerance value, representing the highest acceptable error limit for the calibration results.

[0043] Understandably, the system compares the magnitude of the spatial residual with a preset threshold. If the magnitude is less than or equal to the threshold, it indicates that the calibration error is within the allowable range, the calibration result is reliable, and the system will determine it as valid and output the final calculated calibration coordinates for subsequent use. Conversely, if the magnitude exceeds the threshold, it means the error is too large, and the calibration result is unreliable. In this case, the system will automatically or prompt the user to re-execute the entire data acquisition process starting from physical alignment.

[0044] Step S40: Based on the transformation relationship and the reference coordinates of the target center point, obtain the calibration coordinates of the base control point in the SLAM system coordinate system.

[0045] It's important to note that the core principle behind this step is spatial coordinate transformation. Specifically, using the transformation relationship obtained in the previous step, a point known in the global coordinate system is transformed into the SLAM system coordinate system. Specifically, the reference coordinates of the target center point are the coordinates measured by a high-precision measuring instrument in the global coordinate system, considered as the true values. The transformation relationship precisely describes the rotation and translation relationship between the global coordinate system and the SLAM system coordinate system. By substituting the reference coordinates into this transformation relationship and performing reverse calculations, the corresponding coordinates of the point in the SLAM system coordinate system can be derived.

[0046] Understandably, the physical meaning of this calculation result lies in the fact that it represents the position of the target center point in the SLAM device's viewpoint when the base control point of the SLAM device is precisely aligned with the target center point. Since the base control point and the target center point coincide in space during the physical alignment process, the calculated coordinates of the target center point in the SLAM coordinate system are theoretically equivalent to the calibration coordinates of the base control point in the SLAM system coordinate system.

[0047] This embodiment involves setting up a target at a calibration site and using a high-precision scanner to obtain the reference coordinates of its center point in the global coordinate system; then, physically aligning the SLAM system base control point with the target center and acquiring environmental point cloud data while stationary; then, calculating the transformation relationship between the SLAM system coordinate system and the global coordinate system using a data registration algorithm; finally, based on this transformation relationship and the target reference coordinates, calculating the precise calibration coordinates of the base control point in the SLAM system coordinate system.

[0048] In summary, this technical solution combines high-precision static measurement with SLAM (Simultaneous Localization and Mapping) mobile measurement technology, establishing a reliable coordinate transfer link through physical alignment and point cloud registration. This process effectively transfers the absolute accuracy of the global coordinate system to the SLAM system, ensuring that all subsequent mobile measurement data are unified under an accurate global spatial reference, fundamentally improving the georeference accuracy and reliability of the data produced by the SLAM system.

[0049] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The step of registering the reference information based on the global coordinate system with the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system includes steps S301 to S303: Step S301: The SLAM point cloud data acquired during the static scanning phase is used as the point cloud to be registered, and the coordinate system of the SLAM system itself when acquiring the SLAM point cloud data is defined as the SLAM system coordinate system.

[0050] It should be noted that the point cloud to be registered refers to a single frame of environmental point cloud data acquired by the SLAM measurement system in a static state after physical alignment. At this time, the device is in a stable state, and the point cloud quality is high. Meanwhile, the local spatial frame defined within the SLAM system at this specific moment to describe the relative position of the sensor and its surrounding environment is defined as the SLAM system coordinate system in this calibration process, providing a reference framework for subsequent coordinate transformations.

[0051] Step S302: Obtain the coordinates of at least three target center points in the global coordinate system, which have been measured in advance by a 3D laser scanner, and use the coordinates as reference information.

[0052] It should be noted that these coordinates are the precise coordinates of the target's center point in the global coordinate system, obtained by pre-measuring the entire scene with the target set up using a high-precision terrestrial 3D laser scanner and then processing the data.

[0053] Understandably, the use of at least three target center points is to establish a clear registration correspondence. Matching the three target points with their corresponding points identified in the SLAM point cloud provides essential constraints for calculating the rotation and translation parameters between the two coordinate systems. Only when there are three non-collinear points can a plane be uniquely determined, thus firmly constraining all three rotational and three translational degrees of freedom, and calculating the complete six-parameter rigid body transformation.

[0054] Step S303: Using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target, calculate the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0055] It should be noted that calculating the transformation relationship is essentially solving for an optimal spatial rigid body transformation. This transformation includes rotation and translation, and its purpose is to ensure that all target feature points extracted from the SLAM point cloud, after this transformation, achieve the best spatial alignment with the corresponding target center point coordinates obtained in the global coordinate system in the preceding steps.

[0056] In one embodiment, the target is a black and white checkerboard planar target, and the step of using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target includes: identifying a local point cloud region in the SLAM point cloud data that corresponds to the black and white checkerboard planar target; calculating the equivalent coordinates of the target center point in the SLAM system coordinate system based on the geometric features of the local point cloud region, and using the point cloud features of the equivalent coordinates as the registration target.

[0057] Understandably, the black and white checkerboard planar target in this embodiment serves as a high-contrast calibration tool with a regular geometric pattern. Identifying local point cloud regions refers to the fact that, in the point cloud data captured by the SLAM system during scanning, the target, due to its planar characteristics and the strong reflection differences at the checkerboard corners, forms a data block with clear boundaries and high point cloud density, which can be separated from the surrounding environment through an automatic segmentation algorithm.

[0058] It should be understood that calculating the equivalent coordinates based on geometric features is a crucial processing step. Since point cloud data is discrete and the "center point" cannot be directly derived, this step requires first using a plane fitting algorithm to calculate the equation of the plane containing the target from the extracted local point cloud. Then, by identifying the projection distribution of the checkerboard corner points on this plane, the geometric center of the entire target pattern is calculated. The coordinates of this calculated center point are the equivalent coordinates of the target center in the SLAM system coordinate system, used for subsequent coordinate transformations.

[0059] In a preferred embodiment, calculating the transformation relationship between the SLAM system coordinate system and the global coordinate system includes steps B10 to B40: Step B10: Use the equivalent coordinates of the target center point in the SLAM system coordinate system as the source point set, and use the reference coordinates in the global coordinate system as the target point set.

[0060] It should be noted that the source point set refers to the set of coordinates formed by multiple target center points observed and calculated by the SLAM system within the same measurement scene, in the SLAM system coordinate system. The target point set, on the other hand, refers to the set of coordinates obtained in the global coordinate system by measuring the same physical target center using high-precision surveying instruments such as a total station. These two point sets correspond one-to-one, describing the positions of the same set of physical points in two different coordinate systems.

[0061] Understandably, establishing source and target point sets is a prerequisite for coordinate system transformation. The source point set represents the relatively local observation data that needs to be transformed, while the target point set represents the absolutely significant global reference benchmark to be achieved through the transformation. This method of relating the representation of the same physical entity in different coordinate systems provides the necessary data foundation for solving the spatial transformation relationship between two coordinate systems.

[0062] Step B20: Using the source point set and the target point set as input, apply the ICP algorithm strategy to iteratively solve the transformation parameters until the preset iteration termination condition is reached. The transformation parameters include rotation matrix and translation vector.

[0063] It should be noted that the core of this step is to use the iterative nearest-point algorithm to solve the coordinate transformation. The input to this algorithm is the source point set and the target point set whose correspondence has been established in the previous steps. The transformation parameters are mathematical quantities used to describe the spatial relationship between two coordinate systems. The rotation matrix is ​​used to express the change in direction, that is, the rotation angle relationship between the coordinate systems, while the translation vector is used to express the change in position, that is, the offset of the origin of the coordinate system.

[0064] Understandably, iterative solution is a process of gradually approaching the optimal solution. The algorithm first finds the geometrically nearest point in the target set for each point in the source set as a temporary counterpart. Based on this new correspondence, the algorithm calculates a new rotation matrix and translation vector that minimizes the sum of squared distances between all corresponding points. Then, it updates the position of the source set using these new parameters and repeats the process of finding counterparts and calculating new parameters.

[0065] It should be understood that the preset iteration termination conditions ensure a balance between computational efficiency and result accuracy. Common conditions include the reduction in overall error between two consecutive iterations being less than a threshold, or the number of iterations reaching a set upper limit. When the termination conditions are met, the final rotation matrix and translation vector output by the algorithm are the optimal transformation parameters, which can best align the source point set to the target point set.

[0066] Step B30: Based on the transformation parameters at the termination of the iteration, construct the transformation matrix from the SLAM system coordinate system to the global coordinate system.

[0067] It should be noted that the transformation parameters at the end of the iteration refer to the optimal rotation matrix and translation vector finally determined by the aforementioned algorithm. The transformation matrix is ​​a specific mathematical expression that unifies the two spatial motions of rotation and translation into a single structure, thus enabling the rotation and translation of coordinates to be completed simultaneously in a single operation.

[0068] Step B40: Use the transformation matrix as the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0069] Understandably, establishing this transformation relationship is the ultimate goal and core output of the entire calibration process. It links two originally independent coordinate systems, ensuring the consistency of spatial data. For example, dynamic observation data from a simultaneous localization and mapping (SMR) system can be seamlessly transformed into a unified global coordinate system using this relationship, enabling overlay comparison and analysis with other surveying data or engineering design drawings that use the same global coordinate system.

[0070] This method provides a way to achieve accurate registration between a SLAM system and a global coordinate system using target assistance. First, the method uses high-quality SLAM point clouds acquired in a static state to identify target features and calculate their coordinates in the SLAM local coordinate system. Then, this coordinate set is matched with a pre-obtained set of coordinates of the same target in the global coordinate system obtained through high-precision measurements. Finally, the optimal spatial transformation parameters are solved based on the iterative nearest-point algorithm, thereby establishing a reliable transformation relationship between the two.

[0071] This scheme introduces physical targets with clear geometric features as a common reference, providing a stable and clear correlation benchmark between SLAM local observation data and global absolute coordinates. Utilizing high-quality point clouds from the static phase reduces the impact of dynamic scanning noise on feature extraction, ensuring computational stability. Furthermore, employing multiple non-collinear target points provides sufficient constraints for accurately solving the complete spatial rigid body transformation, thus effectively improving the accuracy and reliability of coordinate transformation and laying a solid foundation for seamless fusion of multi-source spatial data.

[0072] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the calibration method of the SLAM measurement system base control points of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0073] This application also provides a calibration device for the base control points of a SLAM measurement system. Please refer to... Figure 3 The calibration device for the base control points of the SLAM measurement system includes: The data acquisition module 10 is used to set up targets at the calibration site and obtain the reference coordinates of the target center point in the global coordinate system. The data acquisition module 10 is also used to physically align the base control point of the SLAM measurement system with the center point of the target, and to acquire SLAM point cloud data in a static state after alignment. The coordinate transformation calculation module 20 is used to perform data registration with the SLAM point cloud data based on the reference information of the global coordinate system to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system. The coordinate transformation calculation module 20 is also used to obtain the calibration coordinates of the base control point in the SLAM system coordinate system based on the transformation relationship and the reference coordinates of the target center point.

[0074] In one embodiment, the coordinate transformation calculation module 20 is further configured to use the SLAM point cloud data acquired during the static scanning phase as the point cloud to be registered, and define the coordinate system of the SLAM system itself when acquiring the SLAM point cloud data as the SLAM system coordinate system; acquire the coordinates of at least three target center points in the global coordinate system obtained in advance by a 3D laser scanner, and use the coordinates as reference information; use the point cloud features corresponding to the target center points in the SLAM point cloud data as the registration target, and calculate the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0075] In one embodiment, the data acquisition module 10 is further configured to identify local point cloud regions in the SLAM point cloud data that correspond to the black and white checkerboard plane target; calculate the equivalent coordinates of the target center point in the SLAM system coordinate system based on the geometric features of the local point cloud regions, and use the point cloud features of the equivalent coordinates as the registration target.

[0076] In one embodiment, the data acquisition module 10 is further configured to deploy the black and white checkerboard planar target within the calibration site; scan the calibration site containing the target using a high-precision terrestrial 3D laser scanner to obtain high-precision point cloud data; and identify and extract the center point coordinates of the black and white checkerboard planar target from the high-precision point cloud data as the reference coordinates.

[0077] In one embodiment, the coordinate transformation calculation module 20 is further configured to: use the equivalent coordinates of the target center point in the SLAM system coordinate system as the source point set, and the reference coordinates in the global coordinate system as the target point set; use the source point set and the target point set as input, apply the ICP algorithm strategy to iteratively solve the transformation parameters until a preset iteration termination condition is reached, wherein the transformation parameters include a rotation matrix and a translation vector; construct a transformation matrix from the SLAM system coordinate system to the global coordinate system based on the transformation parameters at the iteration termination condition; and use the transformation matrix as the transformation relationship between the SLAM system coordinate system and the global coordinate system.

[0078] In one embodiment, the coordinate transformation calculation module 20 is further configured to calculate the spatial residual between the coordinates of the registered target center point in the SLAM system coordinate system and the reference coordinates; if the magnitude of the spatial residual is less than or equal to a preset threshold, the calibration result is determined to be valid and the calibration coordinates are output; if the magnitude of the spatial residual is greater than the preset threshold, the step of physically aligning the base control point of the SLAM measurement system with the target center point is re-executed.

[0079] The SLAM measurement system base control point calibration device provided in this application adopts the SLAM measurement system base control point calibration method in the above embodiments. It solves the technical problem of how to achieve high-precision calibration of the base control points in the system's internal coordinate system without relying on pose estimation that may have errors within the system itself, thus ensuring the accuracy of all subsequent spatial data conversions from the source. Compared with the prior art, the beneficial effects of the SLAM measurement system base control point calibration device provided in this application are the same as those of the SLAM measurement system base control point calibration method provided in the above embodiments. Furthermore, other technical features in the SLAM measurement system base control point calibration device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0080] This application provides a calibration device for the base control points of a SLAM measurement system. The calibration device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the calibration method for the base control points of the SLAM measurement system described in Embodiment 1 above.

[0081] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of a calibration device suitable for implementing the SLAM measurement system base control points in embodiments of this application. The calibration device for the SLAM measurement system base control points in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The calibration device for the SLAM measurement system base control point shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0082] like Figure 4As shown, the calibration device for the SLAM measurement system base control point may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the SLAM measurement system base control point calibration device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the calibration equipment of the SLAM measurement system base control point to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows calibration equipment of the SLAM measurement system base control point with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0083] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0084] The SLAM measurement system base control point calibration device provided in this application adopts the SLAM measurement system base control point calibration method in the above embodiments. It can solve the technical problem of how to achieve high-precision calibration of the base control points in the system's internal coordinate system without relying on pose estimation that may have errors in the system itself, so as to ensure the accuracy of all subsequent spatial data conversions from the source. Compared with the prior art, the beneficial effects of the SLAM measurement system base control point calibration device provided in this application are the same as the beneficial effects of the SLAM measurement system base control point calibration method provided in the above embodiments, and other technical features in the SLAM measurement system base control point calibration device are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0085] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0087] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the calibration method for the base control points of the SLAM measurement system in the above embodiments.

[0088] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0089] The aforementioned computer-readable storage medium may be included in the calibration equipment of the SLAM measurement system base control point; or it may exist independently and not be assembled into the calibration equipment of the SLAM measurement system base control point.

[0090] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the calibration device of the SLAM measurement system base control point, the calibration device of the SLAM measurement system base control point causes the following: a target is deployed at the calibration site, and the reference coordinates of the target's center point in the global coordinate system are obtained; the base control point of the SLAM measurement system is physically aligned with the target's center point, and SLAM point cloud data is acquired in a stationary state after alignment; data registration is performed based on the reference information of the global coordinate system and the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system; and the calibration coordinates of the base control point in the SLAM system coordinate system are obtained according to the transformation relationship and the reference coordinates of the target's center point.

[0091] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0093] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0094] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the calibration method of the base control points of the SLAM measurement system described above. This solves the technical problem of how to achieve high-precision calibration of the base control points in the system's internal coordinate system without relying on pose estimation that may have inherent errors in the system itself, thereby ensuring the accuracy of all subsequent spatial data conversions from the source. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the calibration method of the base control points of the SLAM measurement system provided in the above embodiments, and will not be repeated here.

[0095] The computer program product provided in this application can solve the technical problem of calibrating the base control points of a SLAM measurement system. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the SLAM measurement system base control point calibration method provided in the above embodiments, and will not be repeated here.

[0096] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for calibrating control points on the base of a SLAM measurement system, characterized in that, The calibration method for the base control points of the SLAM measurement system includes: Targets are set up at the calibration site, and the reference coordinates of the target center point in the global coordinate system are obtained. The base control point of the SLAM measurement system is physically aligned with the center point of the target, and SLAM point cloud data is collected in a static state after alignment. Based on the reference information of the global coordinate system, data registration is performed with the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system; Based on the transformation relationship and the reference coordinates of the target center point, the calibration coordinates of the base control point in the SLAM system coordinate system are obtained.

2. The calibration method for the base control points of the SLAM measurement system according to claim 1, characterized in that, The data registration based on the reference information of the global coordinate system and the SLAM point cloud data to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system includes: The SLAM point cloud data acquired during the static scanning phase is used as the point cloud to be registered, and the coordinate system of the SLAM system itself when acquiring the SLAM point cloud data is defined as the SLAM system coordinate system. Obtain the coordinates of at least three target center points in the global coordinate system, which were previously measured by a 3D laser scanner, and use the coordinates as reference information; Using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target, the transformation relationship between the SLAM system coordinate system and the global coordinate system is calculated.

3. The calibration method for the base control points of the SLAM measurement system according to claim 2, characterized in that, The target is a black and white checkerboard planar target; The step of using the point cloud features corresponding to the target center point in the SLAM point cloud data as the registration target includes: Identify the local point cloud region in the SLAM point cloud data that corresponds to the black and white checkerboard plane target; Based on the geometric features of the local point cloud region, the equivalent coordinates of the target center point in the SLAM system coordinate system are calculated, and the point cloud features of the equivalent coordinates are used as the registration target.

4. The calibration method for the base control points of the SLAM measurement system according to claim 3, characterized in that, The step of setting up targets at the calibration site and obtaining the reference coordinates of the target center point in the global coordinate system includes: The black and white checkerboard-patterned planar target is deployed within the calibration area. A high-precision terrestrial 3D laser scanner was used to scan the calibration site containing the target to obtain high-precision point cloud data. The center point coordinates of the black and white checkerboard planar target are identified and extracted from the high-precision point cloud data and used as the reference coordinates.

5. The calibration method for the base control points of the SLAM measurement system according to claim 2, characterized in that, The calculation of the transformation relationship between the SLAM system coordinate system and the global coordinate system includes: The equivalent coordinates of the target center point in the SLAM system coordinate system are taken as the source point set, and the reference coordinates in the global coordinate system are taken as the target point set. The source point set and the target point set are used as inputs, and the ICP algorithm strategy is applied to iteratively solve the transformation parameters until the preset iteration termination condition is reached. The transformation parameters include rotation matrix and translation vector. Based on the transformation parameters at the termination of the iteration, construct the transformation matrix from the SLAM system coordinate system to the global coordinate system; The transformation matrix is ​​used as the transformation relationship between the SLAM system coordinate system and the global coordinate system.

6. The calibration method for the base control points of the SLAM measurement system according to any one of claims 1 to 5, characterized in that, After physically aligning the base control point of the SLAM measurement system with the center point of the target, and acquiring SLAM point cloud data in a stationary state after alignment, the method further includes: Calculate the spatial residual between the coordinates of the registered target center point in the SLAM system coordinate system and the reference coordinates; If the magnitude of the spatial residual is less than or equal to a preset threshold, the calibration result is determined to be valid and the calibration coordinates are output. If the magnitude of the spatial residual is greater than a preset threshold, the step of physically aligning the base control point of the SLAM measurement system with the center point of the target is repeated.

7. A calibration device for the base control points of a SLAM measurement system, characterized in that, The device includes: The data acquisition module is used to set up targets at the calibration site and obtain the reference coordinates of the target center point in the global coordinate system. The data acquisition module is also used to physically align the base control point of the SLAM measurement system with the center point of the target, and to acquire SLAM point cloud data in a static state after alignment. The coordinate transformation calculation module is used to perform data registration with the SLAM point cloud data based on the reference information of the global coordinate system, so as to obtain the transformation relationship between the SLAM system coordinate system and the global coordinate system. The coordinate transformation calculation module is also used to obtain the calibration coordinates of the base control point in the SLAM system coordinate system based on the transformation relationship and the reference coordinates of the target center point.

8. A calibration device for the base control points of a SLAM measurement system, characterized in that, The calibration device for the SLAM measurement system base control point includes: a memory, a processor, and a calibration program for the SLAM measurement system base control point stored in the memory and executable on the processor. The calibration program for the SLAM measurement system base control point is configured to implement the calibration method for the SLAM measurement system base control point as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a calibration program for the SLAM measurement system base control points. When the SLAM measurement system base control point calibration program is executed by the processor, it implements the SLAM measurement system base control point calibration method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the calibration method for the base control points of the SLAM measurement system as described in any one of claims 1 to 6.

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