# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 4 Experimental Results
### Comparison with Other Methods  
Table 3 presents a comparison between our best model and several state-of-the-art geometry-based [3,4] algorithms as well as learning methods [8, 9, 10, 27, 28, 29, 32]. VISO2 is one of the most widely used geometry-based VO algorithms commonly employed as a baseline for comparison, which uses sparse features for stereo matching to achieve efficient monocular and stereo VO. Monocular VISO2 leverages a fixed camera height to recover absolute scale. ORB-SLAM2, another geometry-based algorithm, has demonstrated impressive results utilizing monocular, stereo, and RGB-D cameras. In our experiments, we denote the monocular and stereo versions of ORB-SLAM2 as \"ORB-SLAM2-M\" and \"ORB-SLAM2-S\", loop-closure detection is disabled while comparison. Among the learning-based approaches, we selected both supervised [9, 10, 27, 32] and unsupervised [8, 28, 29] methods. In the supervised methods, DeepVO takes image sequences as inputs and uses RNN to refer to former ego-motion, while PCNN employs calculated optical flow pairs as input and a sampling network. TartanVO estimates optical flow for subsequent estimation. In the unsupervised methods, GeoNet estimates depth maps and ego-motion to generate rigid flow, which is then compared with predicted optical flow to filter out moving objects in the scene. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Phase** & \multicolumn{8}{c}{**Train**} & \multicolumn{8}{c}{**Test**} \\ \hline Sequence & 00 & 01 & 02 & 03 & 04 & 05 & 06 & 08 & 07 & 09 & 10 \\ Frames & 4541 & 1101 & 4661 & 801 & 271 & 2761 & 1101 & 4071 & 1101 & 1591 & 1201 \\ Total & & & & & 19,308 & & & & & 3893 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sequence detail of KITTI odometry benchmark. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Method** & \begin{tabular}{c} **Seq. 07** \\ _Tent_ \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(R_{rel}\)** \\ **(deg/100 m)** \\ \end{tabular} & \begin{tabular}{c} **\(T_{rel}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(R_{rel}\)** \\ **(deg/100 m)** \\ \end{tabular} & \begin{tabular}{c} **\(T_{rel}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(R_{rel}\)** \\ **(deg/100 m)** \\ \end{tabular} & \begin{tabular}{c} **\(T_{rel}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(R_{rel}\)** \\ **(deg/100 m)** \\ \end{tabular} \\ \hline MG & \
\begin{tabular}{c} VISO2-M [4] \\ ORB-SLAM2-M [18] \\ \end{tabular} & 10.96 & **0.37** & 15.30 & **0.26** & 3.71 & **0.30** & 9.99 & **0.31** \\ \hline StG &
\begin{tabular}{c} ORB-SLAM2-S [18] \\ ORB-SLAM3-S [23] \\ \end{tabular} & **0.59** & **0.20** & 1.8 & **0.18** & 1.67 & 0.37 & 1.35 & 0.25 \\  & ORB-SLAM3-S [23] & 0.62 & 0.21 & **1.16** & 0.19 & **1.02** & **0.34** & **1.24** & **0.25** \\ \hline MU & GeoNet [8] & - & - & 30.57 & 9.89 & 45.14 & 10.37 & 37.86 & 8.17 \\ MU & D3 VO [28] & - & - & 10.40 & **0.75** & 14.24 & 1.21 & 12.32 & **0.98** \\ MU & MLF-VO [29] & 6.11 & 2.59 & **3.64** & 1.33 & 6.39 & **1.14** & 5.38 & 1.68 \\ MS & DeepVO [27] & 3.91 & 4.60 & - & - & 8.11 & 8.83 & 6.01 & 6.72 \\ MS & PCNN [9] & - & - & 6.42 & 2.52 & 19.70 & 3.62 & 13.06 & 7.79 \\ MS & Flowdometry [10] & - & - & 12.64 & 8.04 & 11.65 & 7.28 & 12.15 & 7.66 \\ MS & TartanVO [32] & 8.51 & 4.55 & 7.24 & 2.99 & 9.20 & 2.80 & 8.32 & 3.45 \\ SdS & StereoVO (our) & **3.42** & **1.87** & 5.11 & 1.39 & **5.78** & 1.62 & **4.77** & 1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Odometry results comparison with the state-of-the-art VO methods. The best results of each block are highlighted in bold. M: monocular; St: stereo; G: geometry based; S: supervised; U: unsupervised. ‘-’ means the results are not available from that paper. MLF-VO analyzes how different fusion strategies affect the results and performs ego-motion estimation using RGB and inferred depth information in a Multi-Layer Fusion manner.