# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
## 3 Simulation
### Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & **Sequence** & **00** & **01** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **10** & **Avg** \\ \hline \multirow{4}{*}{MOV} & Tr [\%] & Err & 11.307 & 6.174 & 11.819 & 5.165 & 0.641 & 16.745 & 4.384 & 8.032 & 11.427 & 8.554 & 6.199 & **2.222** \\ \cline{2-13}  & Rot [degg/100] & Err & 3.946 & 2.171 & 4.688 & 2.597 & 0.373 & 10.444 & 2.118 & 5.427 & 3.770 & 2.885 & 5.17 & **3.963** \\ \cline{2-13}  & ATE [m] & Err & 14.531 & 38.289 & 90.335 & 1.545 & 0.232 & 128.369 & 26.280 & 15.427 & 88.199 & 28.789 & 8.642 & **5.1803** \\ \cline{2-13}  & RPE [m] & Err & 0.022 & 0.176 & 0.026 & 0.022 & 0.026 & 0.018 & 0.025 & 0.021 & 0.028 & 0.024 & 0.018 & **0.037** \\ \cline{2-13}  & RPE [deg] & Err & 0.311 & 0.275 & 0.284 & 0.222 & 0.063 & 0.346 & 0.153 & 0.221 & 0.253 & 0.260 & 0.321 & **0.236** \\ \hline \multirow{4}{*}{MOV} & Tr [\%] & Err & 9.769 & 6.201 & 10.767 & 5.135 & 0.738 & 5.524 & 4.396 & 8.106 & 9.678 & 8.564 & 5.886 & **6.797** \\ \cline{2-13}  & Diff & \(-1.538\) & 0.027 & \(-1.052\) & \(-0.050\) & 0.097 & \(-11.221\) & 0.012 & 0.074 & \(-1.749\) & 0.010 & \(-0.313\) & **-1.246** \\ \cline{2-13}  & Rot [degg/100 m] & Err & 3.554 & 2.268 & 4.384 & 2.597 & 0.406 & 2.794 & 1.960 & 5.496 & 3.422 & 2.899 & 4.968 & **3.358** \\ \cline{2-13}  & Rot [degg/100 m] & Err & -0.392 & 0.097 & \(-0.304\) & 0.000 & 0.033 & \(-7.650\) & \(-0.188\) & 0.099 & -0.348 & 0.004 & \(-0.022\) & **-0.085** \\ \cline{2-13}  & \multirow{2}{*}{ATE [m]} & Err & 121.600 & 38.096 & 79.475 & 1.545 & 0.177 & 39.750 & 25.882 & 14.665 & 72.799 & 28.837 & 6.643 & **39.852** \\ \cline{2-13}  & & Diff & \(-22.131\) & \(-0.193\) & \(-10.860\) & 0.000 & \(-0.055\) & \(-88.619\) & \(-0.388\) & \(-0.762\) & \(-15.400\) & 0.148 & \(-1.999\) & **-12.752** \\ \cline{2-13}  & & Err & 0.023 & 0.179 & 0.026 & 0.022 & 0.026 & 0.018 & 0.025 & 0.020 & 0.027 & 0.024 & 0.019 & **0.037** \\ \cline{2-13}  & RPE [m] & Diff & 0.001 & 0.003 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & \(-0.001\) & \(-0.001\) & 0.000 & 0.001 & **0.000** \\ \hline \multirow{4}{*}{Deep VO [45]} & Err & 0.306 & 0.282 & 0.279 & 0.222 & 0.063 & 0.286 & 0.152 & 0.221 & 0.247 & 0.259 & 0.318 & **0.232** \\ \cline{2-13}  & RPE [degg] & Diff & \(-0.005\) & 0.007 & \(-0.005\) & 0.000 & 0.000 & \(-0.140\) & \(-0.001\) & 0.000 & \(-0.006\) & \(-0.001\) & \(-0.003\) & **-0.014** \\ \cline{2-13}  & \multirow{2}{*}{T [\%]} & Err & 65.749 & 138.875 & 11.819 & 81.133 & 0.2053 & 47.975 & 70.602 & 62.13 & 72.711 & 90.195 & 110.799 & **70.186** \\ \cline{2-13}  & Rot [degg/100 m] & Err & 28.745 & 11.612 & 4.688 & 30.119 & 7.19 & 28.675 & 29.824 & 50.996 & 30.599 & 25.633 & 25.814 & **24.900** \\ \hline \end{tabFigure 10: Accumulated yaw angle error and yaw angle of ground truth (GT) and camera calculated by the MVO and the MVO with the proposed method with KITTI 06, 08, and 09 sequences. (**a**) The KITTI sequence 06. (**b**) The KITTI sequence 08. (**c**) The KITTI sequence 09. The left vertical axis is for the yaw angle and the right vertical axis is for the accumulated yaw angle error. Figure 11: Enlarged cornering section of ground truth (GT), yaw angle obtained from the MVO, and yaw angle obtained from the MVO employing the proposed method when KITTI 06, 08, and 09 sequences are used. (**a**) KITTI sequence 06 over timesteps 670–740 is depicted. (**b**) KITTI sequence 08 over timesteps 1290–1340 is depicted. (**c**) KITTI sequence 09 over timesteps 1480–1560 is depicted. Figure 12: Cont.