# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 4 Experiments
### Pose Estimation Comparison  
In this section, we compare the accuracy and robustness of the proposed method with the current state-of-the-art lidar-inertial SLAM methods, including LIO-mapping, LINS, and LIO-SAM. LINS is a filter-based tightly coupled method, and LIO-SAM is a tightly coupled method based on nonlinear optimization. We also use UrbanNav datasets to compare the performance of different methods. Specifically, we used the Urban2019 dataset and Urban2020 dataset. The former was gathered in a typical urban canyon of Hong Kong featuring high-rising buildings and numerous dynamic objects in the evening. The latter was gathered in a low-urbanization area in Hong Kong with some dynamic objects present during in the day, as shown in Figure 7.  
In this experiment, the datasets start and end at the origin. The GPS measurements of the datasets serve as the ground truth. The estimated trajectories of various methods applied to the Urban2019 dataset are shown in Figure 8. LIO-mapping performs well at the beginning of the dataset, but it drifts later in the trajectory and the rotation of LIO-mapping deviates at the curve. This is mainly caused by false feature points belonging to dynamic objects. The trajectories of LINS and LIO-SAM have a similar degree of misalignment, and they both have a slight deviation from the start point to the end point. The overall trajectory of the proposed method performs well. Figure 6: (**a**) the original point clouds; (**b**) semantic segmentation results with point-wise labels; (**c**) point clouds after filtering out dynamic points. Figure 7: Sample images of datasets: (**a**) Urban2019 dataset; (**b**) Urban2020 dataset. Furthermore, we display the 3D position errors and height errors of the different methods with respect to time in Figure 8. The 3D position errors and height errors of the three other methods increase dramatically at one certain point. The 3D position errors of the LIO-mapping approach reach up to more than 100 m, which means that the algorithm has totally failed. By contrast, the 3D position error and height error of our method fluctuate within a certain section since errors accumulate more slowly after moving dynamic objects. The estimated trajectories of the Urban2020 dataset are demonstrated in Figure 9. As shown, all the methods perform better than they did for the Urban2019 dataset, as there are fewer dynamic objects on the road and the distance is shorter. Even so, the proposed  
Figure 8: Estimated trajectories, 3D position errors, and height errors of different methods applied to Urban2019 dataset: (**a**) LIO\(-\)mapping; (**b**) LINS; (**c**) LIO\(-\)SAM; (**d**) our method. method performs better than the LIO-mapping approach with respect to the trajectory. In addition, the 3D position errors and height errors are also lower. Table 1 displays the end-to-end translation errors, end-to-end rotation errors, and RMSE errors with regard to the ground truth. Our method achieves the lowest RMSE error on both datasets. Furthermore, our method is superior to LIO-mapping with respect to end-to-end translation and rotation errors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Error Type** & **LIO-Mapping** & **LINS** & **LIO-SAM** & **Our Method** \\ \hline \multirow{3}{*}{Urban2019} & Translation (m) & 95.473 & 13.258 & 19.274 & **5.673** \\  & Rotation (degree) & 22.829 & 24.704 & 16.942 & **14.554** \\  & RMSE (m) & 43.018 & 7.863 & 13.069 & **5.543** \\ \hline \multirow{3}{*}{Urban2020} & Translation (m) & 5.030 & **1.276** & 3.513 & 3.713 \\  & Rotation (degree) & 14.626 & 25.092 & **6.556** & 7.670 \\ \cline{1-1}  & RMSE (m) & 3.107 & 3.039 & 2.672 & **2.625** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The end-to-end translation errors, end-to-end rotation errors, and RMSE errors of different methods on both datasets. Figure 9: Estimated trajectories, 3D position errors, and height errors of different methods in Urban2020 dataset: (**a**) LIO\(-\)mapping; (**b**) LINS; (**c**) LIO\(-\)SAM; (**d**) our method.