# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Comparative Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**VIO-Method**} & \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{ID01 \({}^{*}\)} & \multicolumn{2}{c}{ID05 \({}^{*}\)} & \multicolumn{2}{c}{ID07 \({}^{*}\)} & \multicolumn{2}{c}{ID02 \({}^{**}\)} & \multicolumn{2}{c}{ID03 \({}^{**}\)} & \multicolumn{2}{c}{ID04 \({}^{**}\)} & \multicolumn{2}{c}{ID06 \({}^{**}\)} & \multicolumn{2}{c}{ID08 \({}^{**}\)} \\  & & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** \\ \hline \multirow{2}{*}{SCP} & RPEt (\%) & **1.70** & **0.90** & **1.17** & **0.63** & **1.10** & 0.52 & **1.35** & **0.82** & **2.14** & **1.66** & **0.10** & **0.53** & **1.11** & **0.56** & **1.24** & **0.75** \\  & RPEt (\(\mathrm{deg}/\mathrm{m}\)) & **1.03** & **0.56** & **0.41** & **0.22** & 0.51 & 0.30 & **0.57** & **0.41** & 0.49 & 0.39 & **0.48** & **0.35** & 0.71 & 0.58 & **5.97** & 5.91 \\  & ATE (m) & **0.98** & **0.38** & **0.20** & **0.08** & **0.19** & **0.08** & **0.33** & **0.33** & **0.37** & 0.11 & **0.22** & **0.08** & **0.11** & **0.06** & **0.19** & **0.09** \\ \hline \multirow{2}{*}{LF} & RPEt (\%) & 2.69 & 1.88 & 1.40 & 0.90 & **1.11** & **0.48** & **1.37** & **0.99** & **1.16** & **0.76** & **0.95** & **0.44** & 1.12 & 0.59 & 1.30 & **0.78** \\  & RPEt (\%) & 1.21 & 0.66 & 0.42 & 0.29 & 0.51 & **0.27** & 0.49 & 0.32 & **0.49** & **0.26** & 0.50 & 0.38 & 0.62 & 0.50 & **5.92** & **5.86** \\  & ATE (m) & 0.98 & 0.38 & 0.27 & 0.13 & **0.21** & **0.08** & 0.39 & 0.15 & **0.27** & **0.11** & 0.31 & 0.13 & 0.11 & 0.06 & 0.25 & **0.08** \\ \hline \multirow{2}{*}{VINS} & RPEt (\%) & 3.72 & 2.47 & 1.70 & 1.05 & 5.47 & 3.67 & 1.66 & 1.13 & 2.38 & 1.85 & 1.56 & 0.89 & 3.54 & 2.31 & 3.37 & 2.66 \\  & RPEt (\(\mathrm{deg}/\mathrm{m}\)) & 1.45 & 1.12 & **0.46** & **0.26** & **0.51** & **0.25** & **0.43** & **0.29** & **0.43** & 0.27 & 0.52 & 0.40 & **0.48** & **0.30** & **6.17** & **6.12** \\ \cline{1-1}  & ATE (m) & 1.34 & 0.50 & 0.36 & 0.14 & 2.85 & 0.65 & 0.52 & 0.21 & 0.71 & 0.30 & 0.42 & 0.13 & 1.49 & 0.18 & 0.47 & 0.19 \\ \hline \hline \end{tabular}  
* denotes the scenario with higher speed. \(**\) denotes the scenario with lower speed. The bold numbers represent the best results in the same scenario.  
\end{table} 
Table 1: Experimental results in the PAL-VIO dataset. Figure 10: The trajectory comparison among SCP-VIO, LF-VIO, and VINS-MONO in slow-moving scenarios (ID01 and ID05) and fast-moving scenarios (ID04 and ID06). Feature point distortion during movement is likely the primary factor contributing to the above phenomenon. For scenario ID01, the rapid motion would lead to varying degrees of distortion in feature tracking, as feature points transition from regions of lower distortion to areas with higher distortion, thereby reducing odometry accuracy. LF-VIO and VINS-MONO process the panoramic image as the planar image during pose estimation, overlooking the inherent distortion in panoramic images, which results in poorer performance. In contrast, the proposed SCP-VIO method mitigates this issue by uniformly segmenting spherical pixels, which maintains consistent feature distortion between adjacent frames. The proposed SCP approach significantly enhances feature tracking accuracy, particularly under high-speed conditions with substantial directional changes, as shown in scenario ID01. The experimental results obtained from the self-built SCP-Image dataset further confirm the advantages of the proposed SCP-VIO method. The corresponding experimental results are shown in Table 3, and the associated CIs at 95% confidence level are shown in Table 4. It is obvious that SCP-VIO consistently outperformed LF-VIO and VINS-MONO. Specifically, SCP-VIO achieved an average improvement of 17.7% over LF-VIO and 15.3% over VINS-MONO in terms of the RPEt.