# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 4 Experiments
### Performance Evaluation  
The performance evaluation of the VO system was carried out on the KITTI Odometry dataset. Only sequence 00-10 in the dataset have ground truth, thus were used for quantitative analysis. Sequence 11-20 were used for additional qualitative analysis. We compared our system with SfMLearner, UnMono, and VISO2-Stereo. Our system, SfMLearner, UnMono are unsupervised deep learning-based. VISO2-Stereo is feature-based. SfMLearner poses were post-processed with ground truth depth information for scale recovery. SfMLearning and UnMono only used RGB images for inference, whereas we used RGB-D images. Since we were only evaluating odometry performance, loop-closure was not deployed. The same parameters were applied for all sequences in terms of each VO system. The KITTY datasets are collected on a moving car. Since the vertical motion of the car is relatively small during driving, we ignore the vertical motion. In other words, in order to better show the effect in the figure, we omitted the z-axis. The trajectories of KITTI Odometry from Sequence 00 to 10 are presented in Figure 5. In Figure 5, the black curves represent the ground truth trajectory. The closer a trajectory is to the black curves, the better the system performance is. It is obvious that our system and UnMono performs better than SfMLearner in all sequences. The deviation mainly occurs when the car makes a turn. In other words, the trajectory is closer to the black curve, and the trajectory is closer to the black curve. Figure 5: Sequence 00–10 trajectories. words, the accuracy of the predicted rotation matrix plays a vital role. Since no loop closure was applied, the noise accumulates and the deviation becomes obvious gradually. From Figure 5d,e,g, we can see the trajectories predicted from our system using additional depth information stay closer to the ground truth than UnMono which use only monocular images. The translation and rotation errors are reduced by more than half. Another point we should notice from Figure 5h,i is that although sequence 09 and 10 were not used for training, the system still performs well on these sequences, which indicates that the model is transferable on other similar scenes. We further carried out a quantitative analysis of the transformation matrix estimation errors between neighboring frames. All errors were calculated based on two consecutive frames. Figure 6 depicts the  
Figure 6: Sequence 00–10 Cumulative histogram of translation error. cumulative histograms of translation error of our VO system, SfMLearner, and UnMono in terms of the aforementioned sequences. These figures show the distribution of translation errors in different ranges. When the translation errors are concentrated at a smaller value, the curves in the cumulative histogram approach the upper left corner, which indicates a better performance regarding translation estimation. In general, our VO system and UnMono perform better than SfMLearner. Furthermore, the maximum translation error of our system is relatively lower. In addition, our VO system is slightly better than UnMono, which is more obvious in Figure 6c-e. In Figure 6c, the translation errors of our VO system are all less than 0.2 m. The translation error of less than 0.2 m in UnMono accounts for 91%, and only 35% in SfMLearner. Figure 7 depicts the cumulative histograms of rotation error of our VO system, SfMLearner, and UnMono. Similar to the evaluation of translation errors, the curves closer to the upper left corner indicate  
Figure 7: Sequence 00–10 Cumulative histogram of rotation error. higher accuracy in terms of rotation estimation. We can see in all sequences, our VO system performs better than UnMono and SfMLearner, which is more obvious in Figure 7a,e,h. If we revisit the trajectories of these 3 sequences from Figure 5, we can tell they have more turns. In other words, our VO system outperforms UnMono and SfMLearner significantly in terms of scenes with more corners.