Underground space mapping method and system based on laser SLAM

Abstract

The invention relates to the technical field of positioning and map construction, in particular to an underground space mapping method and system based on laser SLAM. The method comprises the following steps: acquiring point cloud data and IMU (Inertial Measurement Unit) data of a target space containing an artificial mark point, wherein the artificial mark point is used for enhancing environmental characteristics of the target space; based on the point cloud data, obtaining a laser speedometer factor by using an A-LOAM algorithm, and obtaining an IMU pre-integration factor based on the IMU data; constructing a factor graph based on the laser odometer factor and the IMU pre-integration factor; and optimizing the factor graph by adopting a nonlinear optimization method, and constructing a map based on an optimization result. According to the method, the underground space mapping precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning and mapping. More specifically, the present invention relates to a method and system for underground space mapping based on laser SLAM. Background Art

[0002] With the continuous development of underground engineering construction, the demand for mapping of long and narrow underground spaces such as tunnels, underground parking lots, and mines is increasing. In a long and narrow underground space environment, due to problems such as signal occlusion and multipath effects, it is difficult for a robot to accurately map using GNSS (Global Navigation Satellite System) signals. To address this problem, the current method is to use a SLAM (Simultaneous Localization and Mapping) algorithm that does not rely on positioning information for mapping. Specifically, when a mobile robot is in an unknown environment, it estimates its pose based on environmental information such as distance and images collected by sensors carried on itself, and at the same time builds an environmental map. According to the different sensors carried by the mobile robot, SLAM is divided into laser SLAM and visual SLAM. The visual SLAM algorithm can well collect feature information in an environment with rich texture information, but the visual SLAM algorithm has a large amount of calculation and is greatly affected by light. In an underground space, compared with the visual SLAM algorithm, the laser SLAM algorithm is less affected by light and has a smaller amount of calculation, and can better complete the work of mapping in the underground space.

[0003] However, the underground space usually has highly similar structures, such as tunnels and corridors, with regular shapes and layouts and a lack of obvious unique features, making it easy for laser SLAM to misjudge similar points at different positions as the same feature point when extracting feature points, resulting in feature matching errors.

[0004] Therefore, how to solve the problem of mapping drift caused by scarce environmental features is a technical problem that urgently needs to be solved at present. Summary of the Invention

[0005] To solve the technical problem of low mapping accuracy in underground spaces caused by scarce environmental features as described above, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for mapping underground spaces based on laser SLAM, including: acquiring point cloud data and IMU data of a target space containing artificial marker points, where the artificial marker points are used to enhance the environmental features of the target space; based on the point cloud data, obtaining a laser odometry factor using the A-LOAM algorithm, and obtaining an IMU pre-integration factor based on the IMU data; constructing a factor graph based on the laser odometry factor and the IMU pre-integration factor; optimizing the factor graph using a non-linear optimization method, and constructing a map based on the optimization result.

[0007] Further, obtaining a laser odometry factor using the A-LOAM algorithm includes: acquiring edge points and plane points based on the point cloud data; multiplying an adaptive loss function by a first weight to obtain a first distance, and multiplying a second perpendicular distance from the plane point to the corresponding plane by a second weight, where the independent variable of the adaptive loss function is the first perpendicular distance from the edge point to the corresponding line, the first weight is positively correlated with the difference between the curvature and the average curvature of the edge point, and the second weight is negatively correlated with the second perpendicular distance; solving for the optimal pose transformation matrix at adjacent times based on the first distance and the second distance; and taking the pose transformation matrix as the laser odometry factor.

[0008] Further, the calculation expression for the first weight is: ; In the formula, is the first weight of the th edge point in the current point cloud frame, is the curvature of the th edge point in the current point cloud frame, is the average curvature of all edge points in the current point cloud frame, is the gain coefficient, is the standard deviation of the curvature of the current point cloud frame.

[0009] Further, the calculation expression for the first distance is: ; In the formula, is the first distance from the th edge point in the current point cloud frame to the corresponding line, is the first weight of the th edge point in the current point cloud frame, is the first perpendicular distance from the th edge point in the current point cloud frame to the corresponding line, is the threshold, which is positively correlated with the reference distance, is the Huber loss function.

[0010] Further, the calculation expression of the second weight is as follows: ; In the formula, is the second weight of the th plane point in the current point cloud frame, and is the second perpendicular distance from the th plane point in the current point cloud frame to the corresponding plane. is a scale factor, which is determined according to the actual application scenario.

[0011] Further, it further includes: in response to the first perpendicular distance or the second perpendicular distance exceeding a preset value in continuous preset number of iterations, temporarily excluding the corresponding edge points or plane points when solving the optimal pose transformation matrix.

[0012] Further, before obtaining the lidar odometry factor, it further includes: adjusting the maximum number of iterations in the A-LOAM algorithm multiple times and performing corresponding mapping; taking the maximum number of iterations corresponding to the minimum trajectory error value as the final maximum number of iterations of the A-LOAM algorithm.

[0013] Further, when acquiring the point cloud data, it includes: scanning a target space containing artificial marker points arranged at intervals along the channel direction by a lidar to obtain the point cloud data, and the artificial marker points are reflective films.

[0014] Further, when constructing and optimizing the factor graph, it includes: constructing the factor graph through a preset sliding window and optimizing the factor graph based on the Gauss-Newton algorithm.

[0015] In a second aspect, the present invention provides an underground space mapping system based on laser SLAM, including a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it implements a method for mapping an underground space based on laser SLAM according to the first aspect.

[0016] The beneficial effects of the present invention are as follows: By selecting the maximum number of iterations with the minimum trajectory error as the final maximum number of iterations of the A-LOAM algorithm, the cumulative error caused by long-term operation in a narrow underground space can be reduced, thereby improving the mapping accuracy of the laser SLAM algorithm in the underground space; By adding artificial marker points at regular intervals in the underground space to enhance the features of the underground space, the problem of drift during mapping of the environment due to the scarcity of feature points in the underground space can be avoided, thereby improving the mapping accuracy; By fusing the IMU pre-integration factor and the laser odometry factor, mapping of the underground space can be achieved without relying on GNSS signals, thereby avoiding the problems of chaotic mapping and inaccurate positioning caused by the easy loss of GNSS signals in the underground space, and further improving the mapping accuracy of the underground space; By dynamically allocating weights to the first vertical distance and the second vertical distance, the interference of abnormal points on the optimization process is suppressed, thereby improving the accuracy of pose estimation and further improving the accuracy of subsequent mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart schematically showing a method for underground space mapping based on laser SLAM according to an embodiment of the present invention; Figure 2 is a physical diagram schematically showing a mobile platform according to an embodiment of the present invention; Figure 3 is a flowchart schematically showing the construction of a factor graph according to an embodiment of the present invention; Figure 4 is a block diagram schematically showing the structure of an underground space mapping system based on laser SLAM according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Next, the specific embodiments of the present invention will be described in detail in conjunction with the accompanying drawings.

[0020] Figure 1 is a flowchart schematically showing a method for underground space mapping based on laser SLAM according to an embodiment of the present invention.

[0021] In a first aspect, the present invention provides a method for underground space mapping based on laser SLAM, as Figure 1 shown, the method of the present invention includes: S1. Obtain point cloud data and IMU data.

[0022] Before data acquisition, in the target space (such as a narrow underground space like a tunnel or utility tunnel), along the channel direction, i.e., the device's traveling direction, multiple artificial marking points are set at intervals. Among them, the device mentioned can be a robot or a mobile platform as shown in Figure 2 . Specifically, each artificial marking point (in this embodiment, the artificial marking point is a reflective film) is set on the wall within the range of 0.5 meters to 1 meter from the ground to ensure that the lidar can scan the reflective film. In addition, the reflective film can be set in a diamond pattern (the diagonal length can be 15 cm) to avoid being the same as or similar to the natural structures in the utility tunnel, so as to enhance the uniqueness of the feature points. Further, the retroreflective coefficient of the reflective film used is greater than or equal to 500 cd / lx / m².

[0023] After adding artificial marking points, the mobile platform equipped with a mechanical lidar travels at a constant speed in the target space containing the artificial marking points, while scanning the target space through the lidar to obtain point cloud data; and collecting IMU data through an IMU (Inertial Measurement Unit), where the IMU data includes acceleration and angular velocity.

[0024] In this embodiment, the Leishen Intelligent C16 lidar is used to collect the point cloud data of the target space. The measurement range of this lidar is 100m, and the measurement accuracy is ±3cm.

[0025] By adding artificial marking points in the target space, the environmental features of the underground space are enhanced, solving the problems of mispositioning and chaotic mapping caused by the scarcity of feature points in the underground space, thereby improving the mapping accuracy of the underground space.

[0026] S2. Determine the lidar odometry factor based on the point cloud data and determine the IMU pre-integration factor based on the IMU data.

[0027] In one embodiment, based on the point cloud data, the lidar odometry factor is obtained using the A-LOAM algorithm.

[0028] It should be noted that before determining the lidar mileage factor, it also includes adjusting the maximum number of iterations of the A-LOAM algorithm. Specifically, adjust the maximum number of iterations of the ceres library in the A-LOAM algorithm multiple times and conduct corresponding mapping trajectory experiments, compare the trajectory error results corresponding to several different maximum numbers of iterations, and use the maximum number of iterations corresponding to the minimum trajectory error value as the final maximum number of iterations of the ceres library.

[0029] By selecting the maximum number of iterations corresponding to the minimum trajectory error value as the final maximum number of iterations, it avoids the cumulative error that occurs in traditional SLAM algorithms during long-term operation, shows an obvious drift problem, and thus improves the accuracy of underground space mapping.

[0030] Specifically, the method for determining the lidar odometry factor includes: S201. Obtain edge points and plane points based on the point cloud data.

[0031] Specifically, calculate the curvature of each point in the original point cloud data, and filter out edge points and plane points as key features according to the magnitude of the curvature. Among them, edge points represent points with relatively large curvature in the point cloud, and plane points represent points with relatively small curvature in the point cloud.

[0032] S202. Determine the first distance from the edge point to the corresponding line and the second distance from the plane point to the corresponding plane.

[0033] Specifically, when determining the first distance, for any edge point in the current point cloud frame, determine the two nearest neighbor points in the previous point cloud frame to this edge point, and construct a line with these two points, and then calculate the first distance from this edge point to the corresponding line based on the adaptive loss function and the first weight. Among them, the first weight is positively correlated with the difference between the curvature of the edge point and the average curvature. Specifically, the calculation expression of the first weight is: ; In the formula, is the first weight of the th edge point in the current point cloud frame, is the curvature of the th edge point in the current point cloud frame, is the average value of the curvatures of all edge points in the current point cloud frame (i.e., the average curvature), is the gain coefficient, taking values between 0.5 and 3, is the standard deviation of the curvatures of all edge points in the current point cloud frame.

[0034] It can be understood that the greater the difference in curvature between the edge point and all edge points, the more it indicates that the determined edge point can better represent the environmental characteristics (that is, these points contain more environmental structure information). Therefore, by assigning higher weights to edge points with relatively large curvatures, when calculating the distance from a point to a line in the subsequent process, these key feature points can play a greater role in the optimization process, thereby improving the accuracy of pose estimation, helping the algorithm converge faster, reducing the number of iterations, and thus improving the calculation efficiency.

[0035] In one embodiment, the adaptive loss function can be the Huber loss function. Specifically, the calculation expression of the first distance is: ; In the formula, is the first distance from the th edge point to the corresponding line, is the first weight of the th edge point in the current point cloud frame, is the first perpendicular distance from the th edge point in the current point cloud frame to the corresponding line, is the threshold, which is positively correlated with the reference distance, is the Huber loss function.

[0036] Specifically, The calculation expression of is: In this embodiment, the reference distance is the median of the historical first perpendicular distances. Specifically, is 1.345 times the reference distance. By setting the threshold to be related to the historical data, the loss function is more flexible when dealing with outliers, and it can reduce the estimation bias caused by improper threshold selection, so that the predicted value output by the model is closer to the true value. In an alternative embodiment, the reference distance can also be the mean of the historical first perpendicular distances.

[0037] When the absolute value of the first perpendicular distance from the edge point to the corresponding line is less than or equal to the threshold , the squared loss is adopted, and at this time, the convergence efficiency is high and it is sensitive to normal points; but when the absolute value of the first perpendicular distance from the edge point to the corresponding line is greater than the threshold , it indicates that the edge point is an outlier. At this time, the loss function changes to linear growth, and the gradient of the outlier is limited to a fixed value , rather than increasing infinitely with the error, thus suppressing the gradient weight of the outlier, making the optimization direction more dominated by normal points, thereby avoiding the interference of outliers on pose estimation, and further improving the accuracy and efficiency of determining the optimal pose transformation matrix.

[0038] Meanwhile, for any plane point in the current point cloud frame, determine the three points in the previous point cloud frame that are the closest neighbors to this plane point, construct a plane with these three points, then calculate the second perpendicular distance from this plane point to this plane, and determine the second distance from the plane point to the corresponding plane based on the second weight and the second perpendicular distance, that is, the second distance is obtained by multiplying the second perpendicular distance by the second weight. Among them, the second weight is negatively correlated with the second perpendicular distance. Specifically, the calculation expression of the second weight is: ; In the formula, is the second weight of the th plane point in the current point cloud frame, is the The second perpendicular distance from a planar point to the corresponding plane is a scale factor used to control the attenuation rate of the second weight with respect to the second distance, which is determined according to the actual application scenario.

[0039] It can be understood that the larger the scale factor, the slower the weight decreases, allowing more points with larger distances ( ) to retain their weights, which is suitable for dynamic or noisy scenarios; the smaller the scale factor, the faster the weight decreases, only allowing points with extremely close distances to participate in the optimization process, which is suitable for high-precision and low-noise scenarios.

[0040] Since dynamic objects or noise points are generally far from the corresponding planes, by assigning smaller weights to points far from the planes, the role played by these planar points in the optimization process can be reduced, that is, suppressing the interference of dynamic objects or noise points on solving the optimal pose transformation matrix, making the optimization process more dependent on static planar points (such as the ground or wall surface), thereby improving the accuracy and robustness of point cloud registration, and further improving the accuracy of pose estimation and the precision of subsequent mapping.

[0041] S203. Based on the first distance and the second distance, solve the optimal pose transformation matrix at adjacent times, and use this optimal pose transformation matrix as the lidar odometry factor.

[0042] Based on the above process, calculate the first distance corresponding to all edge points and the second distance corresponding to all planar points, then perform normalization processing, and jointly minimize all the first distances and second distances through the LM algorithm to solve the optimal pose transformation matrix of the lidar. In an alternative embodiment, other non-linear optimization methods can be used, such as the Gauss-Newton algorithm.

[0043] During the optimization process, when the residual of a certain point (edge point or planar point) for three consecutive iterations (the residual corresponding to the edge point is the first perpendicular distance, and the residual corresponding to the planar point is the second perpendicular distance) exceeds the set value (in this embodiment, it is ), it indicates that this point is a dynamic interference or severe noise point, then temporarily remove its constraint until the subsequent point cloud frame resumes stable observation, that is, when the observation residual of these points returns to the normal range in a subsequent frame, they will participate in the subsequent optimization iteration again.

[0044] By setting a value greater than The constraint of this point is removed only when it is ensured that the removal is within a reasonable error range, reducing misjudgment cases and ensuring the inclusiveness of normal static points. By removing only the points that exceed the set value continuously multiple times during the optimization process, accidental noise interference in single-frame data is excluded, and only continuously abnormal dynamic targets are isolated, avoiding misjudgment cases. By allowing the temporarily removed points to re-participate in the optimization after the residuals return to normal in subsequent frames, not only the continuous interference of dynamic objects on pose estimation is avoided, but also the integrity of the environmental map is retained. In summary, by temporarily removing abnormal points, the reliability and accuracy of determining the optimal pose transformation matrix are improved, thereby improving the accuracy of map construction.

[0045] In summary, by dynamically assigning weights to the first vertical distance and the second vertical distance, the interference of abnormal points on pose estimation is suppressed, making the optimization direction more dominated by normal points, improving the accuracy of pose estimation, and thus improving the mapping accuracy.

[0046] In one embodiment, obtaining the IMU pre-integration factor based on IMU data includes: pre-integrating the acceleration and angular velocity measured by the IMU to obtain the change amounts of relative pose, velocity, and rotation between adjacent moments, thereby obtaining the IMU pre-integration factor.

[0047] S3. Construct and optimize a factor graph based on the lidar odometry factor and the IMU pre-integration factor, and construct a map based on the optimization result.

[0048] Fuse the lidar odometry factor and the IMU pre-integration factor to construct a factor graph including pose nodes and constraints. When constructing the factor graph, a sliding window method can be used to limit the scale of the optimization calculation. Specifically, set the size and parameters of the sliding window according to actual needs, and then use this sliding window to construct the factor graph. When a new node is added to the window, the earliest existing node in the window is removed. By constructing the factor graph through the sliding window, not only can the accuracy of the factor graph be ensured, but also the calculation amount can be prevented from being too large to affect the mapping speed, thereby improving the efficiency and accuracy of mapping.

[0049] In the optimization stage, the factor graph is optimized using the non-linear least squares optimization method. In this embodiment, the Gauss-Newton algorithm is used to optimize the factor graph. Specifically, the initial pose and the parameters of the factor graph are set, and then the Jacobian matrix and the residual vector (corresponding to the factor graph optimization error) are calculated. Among them, the Jacobian matrix describes the influence of each factor on the pose, and the residual vector represents the difference between the current pose estimate and the observed value. In each iterative optimization, the current pose estimate is updated according to the Jacobian matrix and the residual vector. Further, after each iteration, it is judged whether the objective function (the sum of the squared errors of all factors) is less than the set convergence threshold. If so, the optimization process ends; if not, the optimization continues. Through multiple iterative optimizations, the finally obtained pose estimate can effectively reduce the drift phenomenon caused by cumulative errors, thereby improving the accuracy and stability of map construction.

[0050] Further, a map is constructed based on the obtained pose estimate. Specifically, according to the optimal estimated values of the feature point nodes obtained by factor graph optimization, the positions of the feature points in the map are determined, the obtained pose estimate is applied to the corresponding point cloud data, the point cloud data at different times and from different perspectives are fused into the same coordinate system, and the point cloud is transformed from the sensor coordinate system to the global map coordinate system, thus completing the map construction.

[0051] Among them, the optimization error of the factor graph is defined as: ; In the formula, is the total number of IMU pre-integration constraints within the sliding window, is the number of edge points extracted from the current point cloud frame, is the number of plane points extracted from the current point cloud frame, is the pose at the th moment in the sliding window, is the pose at the th moment in the sliding window, is the IMU measurement value at the th moment in the sliding window, is the motion model function (IMU pre-integration), representing the predicted pose of the IMU from the th moment to the th moment; is the first weight of the th edge point in the current point cloud frame, is the first perpendicular distance from the th edge point in the current point cloud frame to the corresponding line, is the threshold, is the Huber loss function, is the second weight of the th plane point in the current point cloud frame, is the second vertical distance from the current point cloud frame's th plane point to the corresponding plane, and is the threshold.

[0052] By fusing the IMU pre-integration factor and the lidar odometry factor to construct a factor graph, making full use of their complementarity, the cumulative error is reduced, thereby improving the accuracy of map construction and avoiding the problems of chaotic map construction and inaccurate positioning caused by the easy loss of GNSS signals in the underground space. By optimizing with the sliding window method and the Gauss-Newton algorithm, the computational amount in the map construction process is reduced, the optimization speed is accelerated, and the accuracy of map construction is improved.

[0053] Figure 4 is a schematic block diagram showing the structure of an underground space mapping system based on lidar SLAM according to an embodiment of the present invention.

[0054] In a second aspect, the present invention also provides an underground space mapping system based on lidar SLAM. As Figure 4 shown, the underground space mapping system includes a processor and a memory, and the memory stores computer program instructions, which when executed by the processor implement an underground space mapping method based on lidar SLAM according to the first aspect of the present invention.

[0055] The underground space mapping system also includes other components well known to those skilled in the art such as a communication interface, and its settings and functions are known in the art, so they will not be described in detail here.

[0056] In the present invention, the foregoing memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as, resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), and so on, or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that can be stored or otherwise held by such a computer-readable medium.

[0057] In the description of this specification, "a plurality of" means at least two, for example, two, three, or more, etc., unless otherwise specifically and clearly defined.

[0058] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A method for underground space mapping based on laser SLAM, characterized in that: include: Acquire point cloud data and IMU data of a target space including artificial markers, wherein the artificial markers are used to enhance environmental features of the target space; Based on the point cloud data, a laser odometry factor is obtained using an A-LOAM algorithm, and based on the IMU data, an IMU pre-integration factor is obtained; Constructing a factor graph based on the laser odometry factor and the IMU pre-integration factor; The factor graph is optimized by using a nonlinear optimization method, and a map is constructed based on the optimization results.

2. The underground space mapping method based on laser SLAM according to claim 1, characterized in that: The A-LOAM algorithm is used to obtain the laser odometry factors, including: Acquire edge points and plane points based on the point cloud data; Multiplying an adaptive loss function by a first weight to obtain a first distance, multiplying a second perpendicular distance from the plane point to the corresponding plane by a second weight to obtain a second distance, wherein the independent variable of the adaptive loss function is the first perpendicular distance from the edge point to the corresponding straight line, the first weight is positively correlated with a difference between a curvature of the edge point and an average curvature, and the second weight is negatively correlated with the second perpendicular distance; Based on the first distance and the second distance, solving the optimal posture transformation matrix at adjacent moments; The pose transformation matrix is ​​used as the laser odometry factor.

3. The underground space mapping method based on laser SLAM according to claim 2 is characterized in that: The calculation expression of the first weight is: ； In the formula, The current point cloud frame The first weight of the edge point, The current point cloud frame The curvature of the edge points, is the average curvature of all edge points in the current point cloud frame, is the gain coefficient, is the standard deviation of the curvature of the current point cloud frame.

4. The underground space mapping method based on laser SLAM according to claim 2 or 3, characterized in that: The calculation expression of the first distance is: ； In the formula, The current point cloud frame The first distance from the edge point to the corresponding line, The current point cloud frame The first weight of the edge point, The current point cloud frame The first perpendicular distance from the edge point to the corresponding straight line, is the threshold value, which is positively correlated with the reference distance. is the Huber loss function.

5. The underground space mapping method based on laser SLAM according to claim 2, characterized in that: The calculation expression of the second weight is: ； In the formula, The current point cloud frame The second weight of the plane point, The current point cloud frame The second perpendicular distance from a plane point to the corresponding plane, is the scale factor, which is determined according to the actual application scenario.

6. The underground space mapping method based on laser SLAM according to claim 2, characterized in that: Also includes: In response to the first vertical distance or the second vertical distance exceeding a preset value in a continuous preset number of iterations, the corresponding edge points or plane points are temporarily eliminated when solving the posture transformation matrix.

7. The underground space mapping method based on laser SLAM according to claim 1, characterized in that: Before obtaining the laser odometer factor, it also includes: Adjusting the maximum number of iterations in the A-LOAM algorithm multiple times and performing corresponding mapping; The maximum number of iterations corresponding to the minimum trajectory error value is taken as the final maximum number of iterations of the A-LOAM algorithm.

8. The underground space mapping method based on laser SLAM according to claim 1, characterized in that: When acquiring point cloud data, it includes: scanning a target space including artificial marking points spaced apart along a channel direction by a laser radar to obtain the point cloud data, wherein the artificial marking points are reflective films.

9. The underground space mapping method based on laser SLAM according to claim 1, characterized in that: When constructing and optimizing the factor graph, it includes: constructing the factor graph through a preset sliding window, and optimizing the factor graph based on the Gauss-Newton algorithm.

10. An underground space mapping system based on laser SLAM, characterized in that: It comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an underground space mapping method based on laser SLAM according to any one of claims 1 to 9 is implemented.

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