# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments  
[PERSON]  
1 Department of Automation, Institute of Medical Robotics, Shanghai Jiao Tong University,  
Shanghai 200240, China; [EMAIL_ADDRESS] (J. Related Work  
Researches on global localization mainly focuses on the following two aspects, scene recognition and particle filtering. Observation sensors used for global localization includes visual sensors, two-dimensional(2D) laser sensors and three-dimensional(3D) lidar sensors. Vision sensors and 3D Lidar sensors are more commonly used in outdoor applications. Benefit from the feature of rich observation information, scene recognition is commonly used for visual localization, for instance, using bag of words(BOG) model and its improved method [1; 2]. However, limited by the principle of visual sensors, the effect of visual localization algorithms will be greatly affected if the environment conditions such as light change. Referring to the idea of scene recognition, some researchers focused on the recognition between two frames of laser observation information by extracting and matching the features [3; 4; 5], so as to perform similar effects as scene recognition. Although researchers have done a lot of work on global localization based on scene recognition, there are still some problems such as the calculation complexity of the algorithm and the stability in different environments which are difficult to be completely solved. Therefore, based on features and advantages of the laser sensor, researchers proposed a global localization strategy based on particle filter [6; 7], which is also the most widely used strategy for laser localization. In order to solve the particle degradation problem of the earliest particle filter algorithm [8], researchers proposed some advanced strategies such as importance sampling and Monte Carlo method [9]. These strategies significantly improved the effects of global localization based on particle filter. In order to improve the convergence efficiency and stability of global localization in different application scenes, [PERSON] proposed the concept of active localization. One of the commonly used strategy is entropy-based localization. Since entropy itself is an index for measuring uncertainty [10; 11], the uncertainty of sensor observation will have a great impact on the localization process and results, [PERSON] proposed an active global localization strategy based on entropy increment [12], which reduced the observation uncertainty of sensors by controlling the robot to move to the areas with lower entropy values in the map. Their mechanism reduces the number of particles used by the filter and also the execution time per time slice. However, the problem of entropy-based localization strategy is that the entropy value cannot accurately distinguish the different states of sensor observation. In other words, the area with the same entropy value in the environment may have very different geometric features and scene structure, and the calculation complexity will also be higher if the environment scale gets larger, which also limits the application in large scale scenes. In addition to entropy-based strategies, some researchers have used heuristic rules [13], geometric features extraction [14; 15], and other methods to obtain active paths. While it remains unclear that lower limit for the number of features that have to be present in the environment and how densely these have to be placed. Based on previous research, [PERSON] proposed an active localization strategy based on 2D static localizability for mobile robots based on the 2D laser sensor model and 2D grid map model [16]. This algorithm has been proved to have high particle convergence speed and good real-time performance in indoor structured environments. Support Vector Machine (SVM) is an excellent linear classifier, which has been used in path planning in recent years.