# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 5 Results
### Results and Analysis  
In order to verify the accuracy and robustness of LO with semantic fusion, we compare it with the traditional geometric feature matching LO on KITTI odometry benchmark. Table 1 displays the relative translation errors for different LO algorithm frameworks. The relative translation error values (in meters) measure the error in average displacement within 100 m. Finally, the best-scoring method for each sequence and metric is highlighted in bold text.  
Lidar odometry and mapping (LOAM), a classical SLAM system without loop closure detection; FLOAM, a variant of LOAM, which is a laser SLAM framework that excels in both accuracy and computational efficiency; and Surfel-based mapping (SuMa) with loop closure detection are considered for comparison. It can be seen that our proposed algorithm has higher accuracy compared to LOAM. SuMa shows very good performance in many KITTI scenarios. For example, in the country scenes of sequences 04 and 05, the translational error of SuMa is 0.44 and 0.43 for 100 m, respectively. But the translational error is 1.70 in the highway scenes, whereas the proposed method LO with fused semantic features is 1.27, which is more robust compared to SuMa [36].  
In terms of feature extraction, due to the constraint of semantic information, we can extract higher quality feature points that participate in the next feature matching. At the same time, we can effectively reduce the number of feature points in order to reduce the computational consumption. In Table 2, compared to geometric feature extraction algorithms, semantic-assisted extraction reduces the number of extracted plane and edge features by at least 70% and 51%, respectively. The semantic-assisted extraction algorithm determines the curvature of the point cloud by judging the planar feature aspects at the same time, while the semantic labeling can provide a constraint.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{8}{c}{**Sequences on KITTI Dataset**} \\ \cline{2-13}  & **00** & **01** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **10** & \multirow{3}{*}{**Average**} \\  & **Urban** & **Highway** & **Urban** & **Country** & **Country** & **Country** & **Urban** & **Urban** & **Urban** & **Urban** & **Country** & \\ \hline LOAM & 0.78 & 1.43 & 0.92 & 0.86 & 0.71 & 0.57 & 0.65 & 0.63 & **1.12** & 0.77 & 0.79 & 0.84 \\ FLOAM & 0.81 & 1.34 & **0.82** & 0.80 & 0.76 & 0.57 & 0.61 & 0.61 & 1.14 & 0.76 & 0.78 & 0.82 \\ SuMa & **0.68** & 1.70 & 1.20 & **0.74** & **0.44** & **0.43** & **0.54** & 0.74 & 1.20 & **0.62** & 0.72 & 0.82 \\ Proposed method & 0.71 & **1.27** & 0.95 & 0.75 & 0.69 & 0.55 & 0.67 & **0.59** & 1.16 & 0.74 & **0.71** & **0.80** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the the translation error of different Lidar odometry/slam methods on KITTI odometry benchmark.  
Figure 4: (**a–d**) represent trajectories consisting of initial hypothetical poses obtained from frame-to-frame (FfF) matching. Furthermore, the red trajectory is calculated by frame-to-frame (FfF) matching, while the green trajectory represents the ground truth. (**e–h**) are the final obtained LO trajectories compared with the ground truth.