# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 4 Experiments and Results
### Experiments and Analysis  
In order to obtain the curve localizability map and accessibility map of the scene, we firstly realized map building and pre-processing in the experiment using the intelligent wheelchair platform. We used a Multi-Level ICP Matching Method [25] to perform 3D SLAM based on 3D Lidar and IMU, and obtained a 3D pointcloud map as shown in Figure 6(a). In order to reduce the computational complexity of following steps, we converted the pointcloud map into a OctoMap with a resolution of 0.1 m, as shown in Figure 6(b). On this basis, we extracted the height and normal information of the grassland scene so as to estimate the motion curved surface of the robot and observation states of the lidar. Then the corresponding curve localizability map was calculated according to Formula (4). The results of map pre-processing and localizability map is shown in Figure 8.  
Figure 6: Platform and Environment. Figure 7: Result of Mapping Algorithm. At the same time, we calculated and analyzed the map accessibility [24] for the wheelchair based on the robot model and OctoMap, taken static obstacles, slopes, and collision avoidance as the main factors, we calculated their influence on the robot accessibility in the scene and integrated these factors to generate an accessibility map. The specific process is shown in Figure 9.  
Based on the curve localizability map and accessibility map, we use the wheelchair platform to perform path planning and active localization experiments in the campus grassland scene. In order to test the effect of adding curve localizability index for robot localization, we designed three sets of path planning experiments with fixed starting and ending points. Group 1 just considers the traditional constraints of path planning, group 2 adds the entropy increment index constraint, and group 3 adds the curve localizability index constraint proposed in this paper. The three curves results are shown in Figure 10.  
Figure 8: Results of Pre-Processing and Localizability Map. Figure 9: Result of Map Accessibility Analysis. In order to quantitatively compare the effects of these three paths on robot localization, we use Monte Carlo particle filter algorithm to track the poses of the robot following each path. Since the true values of robot poses are difficult to obtain in this scene, we publish the localization poses in 10 Hz frequency, and record the jump values between each pose during the motion. The final result is shown in Table 1.  
It can be seen that, compared with traditional constraints and entropy increment constraint, after adding curve localizability index, although the path length is partly increased, the localization stability is significantly better than the other two paths, which verify the theoretical rationality and applicability of the curve localizability index. For the active localization experiments, we selected AMCL passive 3D global localization algorithm, straight-line-based active localization method, entropy-increment-based active localization method as comparisons to compare the specific performances of our algorithm on particle convergence efficiency and localization results reliability. In order to ensure the rationality of the experiment, except for AMCL strategy, both the straight-line-based and entropy-increment-based strategies used improved localization strategy in particles initialization and relocation. Setting the same start points, the robot trajectories of these four algorithms are finally shown in Figure 11. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{4}{c}{**Path Planning Strategy**} \\ \cline{2-4}  & **Tradition Constraints** & **Entropy Increment** & **Curve Localizability** \\ \hline pose jumping value(m) number of output poses average of pose jumping(m) & 99.8 & 94.0 & 59.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of different path planning algorithms. Figure 10: Results of Path Planning Experiments.