# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 4 Experimental Results and Analysis
### Large-Scale and Sparse Scenes Test  
Two substations are selected to verify the validity of the MIM_SLAM algorithm in the larger-scale and sparse environments. A general substation comprises thousands of square meters, and its electrical equipment needs to include a sufficient distance for electrical safety. We first tested in substation A shown in Figure 6. During the experiment, the robot traveled approximately 600 m at a speed of 0.6 m/s and the 3D LIDAR sensor collected a total of 10,080 frames. The plan and the environment of substation A are shown in Figure 6. The red line denotes the robot's trajectory ABCDA. The point cloud maps established by the MIM_SALM and the LOAM [12] which has been considered state-of-the-art in LiDAR SLAM are shown in Figure 7.  
Intuitively, the MIM_SLAM algorithm has a high consistency map compared with the LOAM. Due to the accumulated error, the estimated pose has a great deviation when the robot travels from area A along the red track shown in Figure 6, and then returns back to area A. The LOAM cannot effectively handle the data association at this moment, which leads to the wrong association and damage to the established map. In this paper, the multi-level ICP matching method takes into account this situation and generates a reliable constraint between the two poses. The incremental optimization method is used to optimize the global pose. Thus the final map has a high consistency.  
Figure 5: The maps generated in the indoor and outdoor environments.  
Figure 6: The plan and the environment of substation A.  
Next, we tested it in substation B. Compared with substation A, this was larger. Meanwhile, the electrical equipment is placed more sparsely, and less information is gathered by the 3D LIDAR sensor. During the experiments, the robot traveled approximately 1450 m at a speed of 0.6 m/s, and the LIDAR sensor collected a total of 24,220 frames. The plan and the environment of substation B are shown in Figure 8. The red line denotes the robot's trajectory ABCDEABFGDC. The point cloud maps established by the MIM_SLAM algorithm and the LOAM are shown in Figure 9.  
It can be seen from the observation of the two scenes given in Figure 8 that the electrical equipment is distributed more sparsely, and more than 70% of the laser rays reach the maximum measurement range. As shown in Figure 9a, both contour lines of the map established by the MIM_SLAM algorithm are at right angles at areas F and G. Additionally, when the robot returned back to the area C, the details of the point cloud map at area C showed that the telegraph poles and the road edge are clearly visible, and there is no incorrect point cloud accumulation. Because the LOAM cannot effectively process the data association of the closed-loop area, the performance is very poor in this situation, which has more closed-loop areas in large-scale environments, as shown in Figure 9b.  
Figure 8: The plan and the environment of substation B.  
Figure 7: The point cloud maps established by the MIM_SLAM and the LOAM [12] algorithms.