## 4 Comprehensive Performance Evaluation
### Outdoor Environment
#### 4.2.2 Mapping  
1. LiDAR SLAM mapping analysis  
Figure 11 shows LiDAR SLAM mapping in different views and the real scene and the robot trajectory is represented as colored lines. Figure 11a,b are presented as top and side views of the reconstructed map, respectively. Figure 11c provides the real scene of the outdoor environment. By circling around the central platform during data collection, it is evident from Figure 11a,b that the 3D LiDAR reconstruction faithfully captures the complex architectural structures surrounding the platform, including every beam, column, and the stairs on either side. Figure 11: _Cont._  
Figure 10: O4 RPE with regard to the translation part (m) for delta = 1 (frames) using consecutive pairs (with SE(3) Umeyama alignment, while processing panning rotation and scale): (**a**) ORB-SLAM3 with loop closure; (**b**) SC-LeGO-LOAM with loop closure; (**c**) ORB-SLAM3 without loop closure; and (**d**) SC-LeGO-LOAM without loop closure. 2. Visual SLAM based on two mapping methods  
Figure 12 depicts local reconstructed maps based on the ground truth or ORB-SLAM3 within a depth range of 7 m, with the reconstruction areas and perspectives corresponding to the white FOV trapezoids and arrows labeled (1) and (2) in Figure 11.  
Figure 11: LiDAR SLAM outdoor mapping and the real scene. (**a**) Top view of the reconstructed map; (**b**) Side view of the reconstructed map; (**c**) The real scene. In the first perspective, the reconstruction from ORB-SLAM3, Figure 12b, exhibits phenomena of multiple image overlaps on the beams of the building, outdoor air conditioning units, and patterns on the floor. This results in a blurry appearance of objects and the floor patterns appear distorted as a consequence. However, the reconstruction based on ground truth, Figure 12a, while showing slight distortion in some subtle areas, maintains an overall finer and more continuous effect. Moving to the second perspective, Figure 12c,d, similar issues are observed in the reconstruction from ORB-SLAM3, including blurred floor patterns and double-image displacement in the architectural structure. The reason for these problems is consistent with the indoor conditions discussed in the previous section and will not be reiterated here. 3. Comparison between LiDAR SLAM and Visual SLAM  
In this section, a comparison between Visual SLAM and LiDAR SLAM is conducted. By examining the Visual SLAM-based reconstruction map, Figure 13a, and the LiDAR SLAM-based reconstruction map, Figure 13b, we discuss the following three points sequentially. Figure 12: Local reconstructed outdoor maps within a depth range of 7 m. (**a**) The first perspective (1) based on ground truth; (**b**) The first perspective (1) based on ORB-SLAM3; (**c**) The second perspective (2) based on ground truth; (**d**) The second perspective (2) based on ORB-SLAM3. Figure 13: (**a**) Visual SLAM based on ground truth and (**b**) LiDAR SLAM based on 3D LiDAR. The colored point clouds depict the robot’s poses. The white boxes represent specific scenarios: (1.) Reconstruction with loop. (2.) Reconstruction without loop. The first aspect is the difference in the size of the reconstructed area. From the results of both mapping approaches, under the same robot trajectory, the global map reconstructed by Visual SLAM covers only a portion of the LiDAR SLAM global map. This difference arises from the distinct maximum measurement distances provided by the two sensors. In hardware specifications, the optimal measurement depth for the camera is approximately 20 m and exceeding this range introduces larger errors. Therefore, in Visual SLAM reconstruction, the mapping range is set to pixels within a depth of 7 m to ensure more accurate mapping. In contrast, the 3D LiDAR used in this experiment has a maximum scanning range of up to 150 m, resulting in a broader map coverage by LiDAR SLAM while maintaining precision in distant areas. The second aspect is object recognizability.