# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 3 Curve-Localizability-SVM Active Localization
### Active Global Localization  
Based on the active path generated before, we consider to optimize the active localization strategy by combining AMCL algorithm, the most commonly used global localization method. AMCL algorithm was first used for global localization in indoor scenes for mobile robots equipped with 2D laser sensors. Due to the small amount of laser observation data and limited size of scenes, it has shown good results in practical applications. However, in recent years this strategy was extended to 3D Lidar and outdoor environment, the computational complexity of the algorithm has increased significantly because of the increment of the amount of observation data and the number of particles required to estimate the pose of the robot in 3D space.  
Based on the above considerations, we propose improvements in the following two aspects:  
1. Reduce redundant calculations. Since the movement of mobile robots in outdoor environments is constrained by the ground, we can also constrain the state of the particles within the possible curved surface of the robot pose, and re-transform the random distribution of particles in 3D space into a 2D localization problem based on motion curved surface.
2. Improve localization efficiency. By pre-processing of the environment and sensor model, we can perform global localization and relocation, based on a path which can contribute to robot localization, to improve the efficiency of particle convergence.  
Since the height and normal information of the ground can be obtained from 3D pointcloud map, we can determine the pose and corresponding orientation information that the robot may appear in the environment, so that the possible states of the particles can be constrained from 3D space into the motion curved surface during the initialization of global localization. Using this strategy, the number of particles that need to be injected during initialization can be significantly reduced, thereby reducing the redundant calculations. The schematic graph before and after the improvement is shown in Figure 4.  
Meanwhile, considering that the robot pose estimated by particles convergence may not be the correct state, and the previous relocation strategy of AMCL by randomly injecting particles is less efficient, so we add a new relocation strategy into AMCL algorithm based on the matching result judgment between the observation data and the map, similar with pointcloud registration method of ICP algorithm. If the matching score is lower than the threshold, the relocation will start automatically.  
As shown in Figure 5, the white points indicate those lidar points finding successful matches in the map, and the yellow points means that no suitable matches were found. According to the ratio of the number of white points to the total number of points in each observation frame, an appropriate threshold can be set to be used as a standard for judging the reliability of localization results. If the ratio of matching pointcloud is lower than the threshold, it means the global localization result is false, then the algorithm will enter relocation state until obtaining the correct pose.  
Figure 4: Sketch Map of Particle Initialization Improvement.  
Figure 5: Registration Result of 3D Lidar Observation Data.