# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 4 Experimental Results
The hypotheses of this test are as follows:  
* \(H^{a}_{runtime}\), \(H^{a}_{et}\), and \(H^{a}_{et}\) represent no significant difference in runtime, \(e_{t}\) and \(e_{r}\), between the two versions, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SVO + FI + AKT + SKM + VCM + JAF**} & \multicolumn{2}{c}{**SVO + FI AKT + SKM + VCM + Disparity**} \\
**Sequence** & **Runtime (s)** & \(e_{t}\) **(\%)** & \(e_{r}\) **(deg/m)** & **Runtime (s)** & \(e_{t}\) **(\%)** & \(e_{r}\) **(deg/m)** \\ \hline
00 & 0.0729 & 0.9496 & 0.0008 & 0.0703 & 1.3844 & 0.0012 \\
02 & 0.0734 & 1.2011 & 0.0013 & 0.0650 & 1.2042 & 0.0009 \\
03 & 0.0358 & 1.0257 & 0.0005 & 0.0397 & 1.7008 & 0.0010 \\
04 & 0.0546 & 0.5361 & 0.0001 & 0.0600 & 1.6663 & 0.0001 \\
05 & 0.0597 & 0.5957 & 0.0016 & 0.0599 & 1.0291 & 0.0015 \\
06 & 0.0978 & 1.1253 & 0.0006 & 0.0922 & 1.7430 & 0.0011 \\
07 & 0.0585 & 0.6460 & 0.0055 & 0.0612 & 0.8742 & 0.0068 \\
08 & 0.0635 & 2.1540 & 0.0004 & 0.0674 & 2.9020 & 0.0011 \\
09 & 0.0670 & 1.3569 & 0.0015 & 0.0711 & 1.9772 & 0.0015 \\
10 & 0.0744 & 1.7708 & 0.0091 & 0.0752 & 1.7731 & 0.0075 \\ avg & 0.0658 & 1.1361 & 0.0021 & 0.0662 & 1.6254 & 0.0023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed method to a version that determines the AKT window size using only disparity information on the KITTI dataset. (deg/m stands for degrees per meter). * \(H_{runtime}^{b}\), \(H_{et}^{b}\), and \(H_{er}^{b}\) represent a significant difference in runtime, \(e_{t}\) and \(e_{r}\), between the two versions, respectively. As shown in Table 2, the Mann-Whitney U test results demonstrate that \(H_{runtime}^{a}\), \(H_{et}^{b}\) and \(H_{er}^{a}\) are acceptable (\(P_{runtime}=0.7624>0.05\), \(P_{et}=0.0059<0.05\), and \(P_{er}=0.6224>0.05\)), which means that the JAF can significantly improve the robustness and accuracy without  
Figure 7: Reconstructed path for (**a**) sequence 00; (**b**) sequence 03; (**c**) sequence 05; (**d**) sequence 07; (**e**) sequence 08; and (**f**) sequence 10.  
increasing the runtime. This beneficial behavior is mainly because the translation velocity and the yaw angle in the JAF have already been computed in the previous frame. Moreover, on sequences 05 (with corner loops) and 07 (without corner loops), the comparison of the average processing times at every stage between our method and a version without the FI and VCM that performs SURF detection, feature-based stereo matching, AKT, and SKM at each frame is shown in Table 3. It can observe that the proposed method helps reduce runtime significantly, and the times spent on feature detection, stereo matching, and motion estimation are reduced by approximately 3 times. In conclusion, the total processing times are reduced by more than 40%. For completeness, several real-time systems were compared in the subsequent experiments in order to evaluate the performance of the proposed algorithm. They included the SOFT2 [4], the most accurate SVO; the ORB-SLAM2 [11], a complete SLAM system that has four parallel threads; the FRVO [7], the fastest single-thread SVO on the KITTI leaderboard until now; the SVO-FPGA [12], a multiple master-slave FPGA architecture for a SIFT-based SVO; and the VOLDOR [13], a dense indirect VO based on GPU. Table 4 shows the runtime, the average translation error, and the average rotation error of the proposed method compared to other methods, using always the results published by the original authors. Although the proposed approach is slightly less accurate than SOFT2, the ORB-SLAM2, and the FRVO, all can provide very accurate estimations. In particular, the proposed method outperforms in terms of runtime. The proposed approach is 34% better than the SOFT2 and is similar to the ORB-SLAM2. However, the ORB-SLAM2 splits SLAM into four parallel threads and is more costly, while the proposed SVO is a single-thread system.