# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 4 Results and Discussion
### Results
This means that a larger sequence will have a larger error, due to the drift error associated to local odometry algorithms. Thus, it is not straightforward to combine the results obtained through all KITTI sequences. Nonetheless, since the RPE metric computes the error for each pair of poses in each subsequence of 100 m, it is possible to better analyze the combined results of each method. As shown in Table 2, RPE error results are smaller as this metric measures the error along each subsequence of 100 m. Consequently, Figure 3 contains a box-plot chart from the data obtained with the RPE metric. To generate this chart, large outliers were removed to visualize the whole data with the proper quality. In the figure, it can be observed that while Configurations A-C provide similar results (as shown in Tables 1 and 2), the differences among the rest are more significant. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Configuration**} \\ \cline{2-7}  & **Sequence** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \multirow{8}{*}{Translation (m)} & 00 (Urban) & 1.38 (0.87) & 1.39 (0.85) & 1.38 (0.85) & 1.52 (0.73) & **1.26 (0.79)** & 2.89 (1.72) \\  & 01 (Highway) & 5.93 (14.58) & 5.93 (14.90) & 5.97 (14.99) & 430.36 (472.06) & 9.83 (23.05) & **1.78 (0.75)** \\  & 02 (Urban) & 7.68 (22.98) & 7.71 (23.05) & 7.66 (22.92) & 9.28 (27.71) & **2.58 (5.00)** & 45.84 (32.42) \\  & 03 (Countury) & 0.92 (0.44) & 0.93 (0.45) & **0.92 (0.44)** & 1.29 (0.49) & 0.97 (0.48) & 4.69 (2.41) \\  & 04 (Countury) & **1.25 (0.31)** & 1.27 (0.31) & 1.27 (0.31) & 2.09 (2.26) & 1.39 (0.37) & 4.37 (5.13) \\  & 05 (Countury) & 1.26 (0.53) & 1.26 (0.54) & 1.26 (0.54) & 1.44 (0.59) & **1.16 (0.57)** & 1.44 (0.80) \\  & 06 (Urban) & 1.21 (0.43) & 1.21 (0.43) & 1.20 (0.43) & 1.41 (0.52) & **1.12 (0.48)** & 1.36 (0.75) \\  & 07 (Urban) & 1.10 (0.59) & 1.09 (0.59) & **1.09 (0.59)** & 1.24 (0.59) & 1.23 (0.68) & 2.42 (1.40) \\  & 08 (Urban) & 1.48 (0.70) & 1.49 (0.70) & 1.49 (0.71) & 1.68 (0.81) & **1.34 (0.72)** & 4.77 (5.84) \\  & 09 (Urban) & 1.28 (0.41) & 1.27 (0.42) & 1.28 (0.42) & 1.62 (0.55) & **1.11 (0.48)** & 60.84 (23.32) \\  & 10 (Countury) & 1.44 (0.52) & 1.43 (0.52) & 1.43 (0.52) & 1.59 (0.48) & **1.28 (0.59)** & 1.63 (1.36) \\ \hline \multirow{8}{*}{Rotation (\({}^{\circ}\))} & 00 (Urban) & **1.28 (0.76)** & 1.29 (0.76) & 1.28 (0.75) & 1.36 (0.68) & 1.46 (0.70) & 4.10 (1.85) \\  & 01 (Highway) & 0.92 (0.77) & 0.89 (0.71) & 0.89 (0.69) & 30.77 (23.92) & **0.82 (0.52)** & 1.05 (0.63) \\ \cline{1-1}  & 02 (Urban) & 3.35 (7.18) & 3.37 (7.21) & 3.35 (7.18) & 3.73 (7.29) & **1.71 (1.80)** & 79.58 (54.90) \\ \cline{1-1}  & 03 (Countury) & 0.92 (0.34) & 0.92 (0.33) & 0.92 (0.33) & 1.25 (0.40) & **0.90 (0.35)** & 3.62 (2.31) \\ \cline{1-1}  & 04 (Countury) & **0.47 (0.25)** & 0.50 (0.22) & 0.50 (0.22) & 1.08 (0.54) & 0.53 (0.28) & 7.18 (0.05) \\ \cline{1-1}  & 05 (Countury) & **1.00 (0.58)** & 1.01 (0.58) & 1.01 (0.58) & 1.19 (0.69) & 1.32 (0.65) & 2.34 (0.92) \\ \cline{1-1}  & 06 (Urban) & 0.87 (0.41) & 0.86 (0.41) & **0.86 (0.41)** & 1.05 (0.41) & 0.87 (0.54) & 1.6 (0.79) \\ \cline{1-1}  & 07 (Urban) & 1.08 (0.46) & 1.08 (0.46) & **1.07 (0.46)** & 1.09 (0.48) & 1.69 (0.63) & 4.17 (1.30) \\ \cline{1-1}  & 08 (Urban) & 1.26 (0.69) & **1.26 (0.69)** & 1.27 (0.69) & 1.50 (0.81) & 1.41 (0.66) & 9.50 (13.50) \\ \cline{1-1}  & 09 (Urban) & 0.91 (0.52) & **0.91 (0.51)** & 0.91 (0.52) & 1.33 (0.64) & 1.15 (0.59) & 100.13 (54.49) \\ \cline{1-1}  & 10 (Countury) & 0.95 (0.59) & **0.95 (0.60)** & 0.95 (0.60) & 1.08 (0.59) & 1.02 (0.57) & 2.55 (3.82) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative Pose Error (RPE) on the different KITTI odometry sequences for each configuration.