# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 4 Results and Discussion
### Discussion  
From the deep study that was performed on the results obtained from the experiments, it is possible to extract several realizations. First, the number of points that are reduced in the filtering step is an important factor in the odometry. As presented in Figure 2, the downsizing in configurations where dynamic vehicles are removed is very small, and the resulting size is around 99% of the original size. This is the reason the errors obtained from these configurations are very similar to the baseline. On the other hand, configurations with a heavy filter step (Ground and Structure) show more variations in the results. This is the case when we only keep structures and the point cloud is reduced up to 10% of its size. These configurations are very sensitive to the environment; therefore, environments with no structures will have several continuous frames where the odometry is completely lost. Nonetheless, these configurations also show some cases where their performance is highly superior to the baseline. A possible explanation for this is that only the most significant points are kept, while most of the noise sources are removed from the input. As a final remark, it is to be considered that smaller point clouds mean fewer points to be processed, thus reducing the computational time and allowing a higher output rate, as demonstrated with the results presented in Figure 4.  
Although the disparity with the baseline is small in both downsizing and odometry error, the obtained results show a slight improvement for configurations where dynamic objects are removed. Additionally, the configuration where all dynamic objects are filtered also has better results than Configuration C, where only the dynamic vehicles are removed. This fact proves that pedestrians are also a source of noise in the odometry algorithm. As explained above, this result was expected and confirms our initial hypothesis. Nevertheless, because of the almost imperceptible change from the raw point cloud, the slight improvement that is appreciated may not be substantial enough to lead to a solid conclusion. A different dataset with a more crowded environment and a higher number of dynamic objects would be more suitable to finally consolidate this assertion. Regarding Configuration D, which filters out all points with a distance from the sensor greater than 30 m, the obtained results quickly lead to a straightforward verdict: Even if the information in the point cloud is sparse and far from the vehicle, it is still useful for the odometry. This configuration consistently presents worse results than the baseline, which could be explained by the fact that having far references in the matching may prove helpful for the rotation computation. Another interesting observation in the study is the variability in the results from the last configuration, where only the structures and objects are kept in the point cloud. These results are generally bad, with some noteworthy exceptions. On the one hand, because of the considerable reduction of the cloud size, the odometry algorithm encounters many areas with almost no points. This happens in roads without buildings around and with a limited number of traffic signs that could be used as references. On the other hand, this configuration works remarkably well in the highway sequence (Sequence 01). This case is of special relevance, because highway environments are the most challenging for SLAM algorithms, due to the small number of structures, the high speed of the ego-vehicle and the dynamic movement of all other vehicles. After analyzing the original point cloud, we noticed the presence of guardrails, which are labeled as \"fence\" and are considered structures. With only the help of these structures and traffic signs, the odometry algorithm is capable of outperforming all other methods.