## 4 Experiment
### Indoor Experiment 1  
Indoor experiment 1 was carried out in a corridor with few feature points. Since there was no GPS signal indoors, SLAM was mainly carried out through the tight coupling of dual lidar odometry and IMU. To highlight the performance of the HSDLIO algorithm, LeGO-LOAM and LIO-SAM algorithms are compared with it. Figure 7a shows the environment map of the corridor, while Figure 7b-d shows the overall mapping of LeGO-LOAM, LIO-SAM, and HSDLIO algorithms in the corridor scene, with the white points representing backpack motion trajectories. Compared with the HSDLIO algorithm in Figure 7d, the overall mapping of LeGO-LOAM (Figure 7b) has a significant deviation and the structure is unclear. Figure 8a,b shows that the mapping of LeGO-LOAM is incomplete because the lidar has a considerable drift in a scene with fewer feature points. The mapping results of LIO-SAM (Figure 7c) is close to the real environment, with its structure complete. The main reason is that LIO-SAM tightly couples the horizontal lidar and IMU to make up for the lidar drift. However, Figure 9a,b indicates the mapping result of HSDLIO is more abundant than LIO-SAM in the top surface and ground information. The reason is that HSDLIO fuses horizontal and vertical lidars so that its angle of FOV in both horizontal and vertical directions is 360\({}^{\circ}\). The dual lidars and IMU are tightly coupled to make up for the lidar drift. Figure 7: (**a**) Corridor scene for experiment 1. (**b**) Mapping results of LeGO-LOAM in the corridor scene, with the mapping of LeGO-LOAM having a significant deviation and the structure incomplete. (**c**) Mapping results of LIO-SAM in the corridor scene, with LIO-SAM having a complete mapping structure but lacking information on the top and ground. (**d**) Mapping results of HSDLIO in the corridor scene, with HSDLIO having a complete mapping structure. In the long corridor scene, compared with LeGO-LOAM and LIO-SAM, the structure of HSDLIO mapping is more complete and the point cloud information on the ground and top surface is richer. To verify the positioning accuracy of the HSDLIO, the trajectories of LeGO-LOAM, LIO-SAM, and HSDLIO are compared in Figure 10. It can be observed in Figure 10 that the LeGO-LOAM trajectory has drifted. The main reason is that in the LeGO-LOAM algorithm, the lidar odometry error continues to increase in a long corridor environment with fewer feature points. Both LIO-SAM and HSDLIO use the tight coupling of IMU and lidar for positioning, which improves positioning accuracy. HSDLIO further improves the accuracy of positioning through the tight coupling of dual lidars and IMU. To further improve the positioning accuracy of HSDLIO, the relative translational error (RTE)--the distance from the start point to the end point--is introduced. In all experiments, the data collection started and ended at the same point; when the positioning accuracy of  
Figure 8: Comparison of mapping details at the corner of the corridor, where (**a**) the mapping of LeGO-LOAM has a significant deviation and (**b**) the mapping of the HSDLIO structure is clearer and more complete than LeGO-LOAM. Figure 9: Comparison of mapping details at the top of the corridor, where (**a**) the mapping of LIO-SAM lacks top information and (**b**) the mapping of HSDLIO shows finer structural details of the environment. the device is very high, the start and end point will coincide. The RTE, when the backpack returns to the start point, is shown in Table 1. The RTE can be computed by Equation (10):  
\[\mathbf{RTE}=\sqrt{\mathbf{x}_{\mathbf{o}}^{2}+\mathbf{y}_{\mathbf{o}}^{2}+\mathbf{z}_{\mathbf{o}}^{2}} \tag{10}\]  
where \(\mathbf{x_{o}}=\mathbf{x_{s}}-\mathbf{x_{e}}\), \(\mathbf{y_{o}}=\mathbf{y_{s}}-\mathbf{y_{e}}\), \(\mathbf{z_{o}}=\mathbf{z_{s}}-\mathbf{z_{e}}\), \(\mathbf{x_{s}}\), \(\mathbf{y_{s}}\), and \(\mathbf{z_{s}}\) are the coordinates of the starting point and \(\mathbf{x_{e}}\), \(\mathbf{y_{e}}\), and \(\mathbf{z_{e}}\) are the coordinates of the end point.