# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
Different obstacle points are set as \(+1\) or \(-1\); the optimal classification interface between different categories found by SVM can be well applied to path planning. At the same time, if these points are not linearly separable, they can be mapped to high-dimensional space by kernel function to find a suitable classification hyperplane in high-dimensional space, which may correspond to any shape curve in low dimensional space [17]. The path planning based on SVM can optimize the path plan and reduce the influence of robot size. [PERSON] et al. proposed a novel solution AgRoBPP- bridge based on SVM for path planning in agricultural robots, and it demonstrated the best performance in terms of precision and computational resources consumption [18]. [PERSON] et al. proposed a novel path planning method using SVM and Longest Accessible Path with Course Correction (LAP-CC), and simulation experiments showed that it had the advantages of simplicity, feasibility, low computational cost and good repeatability [19], [PERSON] et al. proposed a novel local path planning method based on SVM [20]. In this method, the registration of the point clouds is accomplished via the exploitation of the localization, the FPFH features and the ICP. Relying upon stereo vision and v-disparity images, the robot can safely navigate for a long distance outdoors. [PERSON] et al. used SVM for path planning in unknown environment [21], and proved the effectiveness of the method through experiments. [PERSON] et al. proposed a method combining a basic path subdivision method for topological maps of local environments and SVM [22]. These methods are effective, but the influence of uneven ground and the limitation of grade ability are not taken into account. For example, although some slopes are not blocked by obstacles, they are still impassable for robots. All these methods mentioned above solve some specific problems for mobile robots active localization, but it's still difficult to satisfy the practical application needs when the scene is expanded to outdoor, uneven-ground, feature-complex environments especially wild areas. In terms of these issues of current active localization algorithms, considering that the pose, orientation and observation state of the robot on uneven ground cannot be accurately described by the three degrees of freedom(DOFs) on 2D surface, this paper proposes a curve-localizability-SVM active global localization strategy for mobile robots in outdoor environments so that the robot can autonomously choose an active path which is conducive to robot localization, and improves the efficiency and stability of global localization. The framework of our algorithm is shown in Figure 1. In the preprocessing stage, 3D OctoMap and 3D Lidar Model are used to generate curve localizability map and accessibility map. These maps will be used for path planning and tracking. In real-time stage, odometer and 3D Lidar data are used by particle filter algorithm to locate. In Section 2, we will introduce the curve localizability index. Based on this index, Section 3 will perform active localization path planning considering localizability constraint and other traditional constraints, and then propose the curve-localizability-SVM active localization strategy combining with the improved AMCL algorithm(Adaptive Monte Carlo Localization). Experimental verification and results analysis will be described in Section 4. Finally, Section 5 is the summary and prospects of this paper.