# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 4 Experimental Results  
In this section, the proposed approach was evaluated using the publicly available KITTI odometry dataset, which is composed of captured videos along with an accurate ground truth. The rectified stereo images with a size of 1241\(\times\)376 are recorded at a frequency of 10 Hz. In the following experiments, for each training sequence of the KITTI dataset except sequence 01, thirty trials were conducted, and the average translational error \(e_{t}\), the average rotational error \(e_{r}\) relative to the ground truth, and the runtime per frame were employed as the performance metrics. For sequence 01, a highway scenario with largely distant image areas driving at high speed does not apply to the KLT tracker. Therefore, the performance of this method was evaluated on the other 10 training sequences,  
Figure 6: The veers \(A\) and \(B\) in the trajectory (**left**) correspond to the peaks \(a\) and \(b\) in the yaw angle diagram (**right**), respectively. which included urban and rural scenarios. All of the experiments were performed using a PC with an Intel Core i5 9500 3.0 GHz processor and a 16 GB RAM using a single thread. In order to prove that the improved algorithm could greatly reduce the computational complexity without notably compromising the localization accuracy, MATLAB was used to conduct the simulation experiments on the prototype of the algorithm (there is no code optimization). Even so, the system could run at 15 Hz. Considering the trajectory, Figure 7 shows the path reconstructed from our SVO compared to the ground truth data on several sequences of the KITTI dataset. They have the same shapes. Table 1 shows the average translation error and rotation error on the 10 training sequences. Although sequences 01 and 05 with corner loops have 3723 m and 2204 m of traveling, respectively, the proposed SVO with the JAF and the VCM can obtain an average translation error of 0.9496% and 0.5957%, and a rotation error of 0.0008 deg/m and 0.0016 deg/m, respectively. Meanwhile, sequences 03 and 07 without corner loops have a shorter path (561 m and 695 m, respectively); hence, the proposed approach also has a high localization accuracy, with an average translation error of 1.0257% and 0.6460%, and a rotation error of 0.0005 deg/m and 0.0055 deg/m, respectively. The results in Figure 7e,f have been obtained without the VCM scheme for a long path, which leads to a slightly worse error. However, there is no difference in runtime. This means that the proposed VCM can greatly improve the localization accuracy while not sacrificing time performance. Furthermore, the proposed method was compared to a version that determines the AKT window size using only disparity information, as shown in Table 1. One can observe that both the JAF and the VCM help to significantly improve the localization accuracy. On average, the translation and rotation errors on the 10 train sequences are (1.1361%, 0.0021 deg/m) and (1.6254%, 0.0023 deg/m), respectively. Therefore, the AKT using the JAF performs 30% better than the AKT using only disparity information, while the JAF does not require extra runtime. To further evaluate the improvement in time performance, Table 1 shows the average runtime per frame of the 10 training sequences. Thanks to the FI, the AKT, and the SKM, the proposed SVO thread runs at 0.0658 s per frame with a standard deviation of 0.0161 s. This means that the proposed SVO can run in real time at 15 Hz on the KITTI odometry dataset. The deviation is mainly caused by the differences in the number of scenario features and corner loops. Because the data in Table 1 are normally distributed, a Mann-Whitney U nonparametric test was used to further analyze the differences in runtime, \(e_{t}\) and \(e_{r}\), between the two versions. The test was performed with the help of an SPSS v24 computer program using a 95% confidence level.