# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 5 Experimental Evaluation
### Discussion of the Parameter and Performance  
In our experiment, we define the thresholds \(c_{th}\) and \(p_{th}\) based on the KITTI Odometry sequence 01. Laser scans contained in this dataset are collected from a highway, the scenario where there is only a very few distinct structural features could be used for scan matching. To extract as many as possible features to accomplish the SLAM task, the thresholds should be set to a fairly low level. It is because the smaller the thresholds, features on spheres or cylinders are more likely to be extracted. These sphere and cylinder features themselves are not planar, and if represented by planes directly can bring about errors that would further reduce the accuracy of the downstream algorithms. In this paper, we blend each planar patch extracted with the weighted function under the implicit surface representation mechanism. This explains why we achieve obvious improvement on this sequence. Therefore, if the thresholds can perform well on such structural feature lacking scenarios, they should be easily transferred to and work in scenarios with richer structural features. For more structured environment such as urban case, the thresholds can be further increased to compromise between the feature richness and the planarity, which promises an accuracy improvement to the whole SLAM algorithm. Table 2 shows the influence of \(c_{th}\) to the result. When \(c_{th}\) is set too large, we can not extract enough features and the result gets worse. Table 3 shows the influence of \(c_{th}\) for ground features to the result. The quantity \(c\) as shown in (3) of ground features is generally large and the threshold \(c_{th}\) has a slight influence on the result. Figure 5 shows the variation of the number of non-ground features in different frames on sequence 01 when \(c_{th}\) is set to 0.85. The average number of non-ground features is about 7200. [PERSON] (2018) holds the view that the pose estimation result is related to the number and distribution of features. The features of different position contribute to different angles and translations. They sample 1000 features based on their sampling strategy which considers the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **Environment** & **LOAM** & **SUMA** & **Our SLAM** \\ \hline
0 & Urban & 0.78\% & 0.7\% & 0.64\% \\
1 & Highway & 1.43\% & 1.7\% & 0.90\% \\
2 & Urban + Country & 0.92\% & 1.2\% & 0.74\% \\
3 & Country & 0.86\% & 0.7\% & 0.59\% \\
4 & Country & 0.71\% & 0.4\% & 0.49\% \\
5 & Urban & 0.57\% & 0.4\% & 0.43\% \\
6 & Urban & 0.65\% & 0.5\% & 0.36\% \\
7 & Urban & 0.63\% & 0.7\% & 0.35\% \\
8 & Urban + Country & 1.12\% & 1.2\% & 0.84\% \\
9 & Urban + Country & 0.77\% & 0.6\% & 0.53\% \\
10 & Urban + Country & 0.79\% & 0.7\% & 0.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results On KITTI Odometry. Figure 4: Sample results using the KITTI benchmark datasets. The datasets are chosen from three types of environments: highway, country and urban from left to right, corresponding to sequences 01, 03, and 07. In (**a–c**), we compare estimated trajectories of the vehicle to the GPS/INS ground truth. The mapping results are shown in (**d–f**). An image is shown from each dataset to illustrate the environment, in (**g–i**). feature distribution and achieve great result. Compared with their method, we do not use a sampling strategy but use more features which compensate for the influence of feature distribution on the results to a certain extent. In our method, the variance of the feature numbers has an influence on the extracted feature distributions and thus further influences the performance. However, since we extract enough number of features for scan-matching, which is more than 2000 at least, there is little influence on the translational error when the number of features drops. The parameter \(n\) is set to 40 for the KITTI odometry benchmark. Table 4 shows the the influence of parameter \(n\).