Abstract
Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.
Highlights
As an important traditional energy industry in China, the coal industry is an important part of China’s national economy
Due to advantages of intuitive mapping, high ranging accuracy, unaffected by the variation of illumination and view angle, and its ability to operate in all weather conditions [11], Lidar is widely used in the field of unmanned driving [12,13,14,15] and is more suitable for Simultaneous localization and mapping (SLAM) in Complex and changeable coal mine environments with poor light conditions
Ren Z. et al [17] studied the lightweight loop detection and optimization algorithm based on rules and Generalized Iterative Closest Points (ICP) (GICP) [18], and proposed the SLAM optimization method based on GICP 3D point cloud registration, but the positioning and mapping accuracy still needs to be improved
Summary
As an important traditional energy industry in China, the coal industry is an important part of China’s national economy. Giseop Kim and ayoung Kim of KAIST University in Korea proposed a scan context loopback detection algorithm [30], which uses a non-histogram global descriptor to realize fast and effective search and matching of current and historical frame data It has the characteristics of high precision, and low time-consumption and computational costs. In view of the real-time and accuracy requirements of map construction of the coal mine underground environment, and considering the high-precision, low-cost, efficient and robust characteristics of the scan context, this paper uses a scan context algorithm to optimize the LeGO-LOAM loopback detection module, and uses an ICP algorithm to optimize the global map obtained by loop, and proposed a LeGO-LOAM-SC SLAM algorithm fusing Scan Context and LeGO-LOAM to improve the accuracy, real-time and robustness of coal mine underground map construction, and evaluated the performance of the proposed algorithm with the KITTI data set 00 sequence data and the point cloud data collected experimentally in an underground simulation scene, so as to explore a better SLAM algorithm and to provide technical support for map construction and unmanned driving of the coal mine underground environment. To give consideration to real-time and accuracy, a lower frequency loop detection is adopted, and there is still a large cumulative error in the mapping of long-range and large scenes
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