Kidnapping and Re-Localizing Solutions for Autonomous Service Robotics

摘要

Indoor localization is one of the most important technologies for a mobile robot working in indoor environments. Laser-based robot localization methods provide good performance but is still problematic for kidnapping and re-localization issues. Monte Carlo Localization (MCL) has been introduced as a useful method that can deal with kidnapping and re-localization problems. However, MCL requires long computational time when the robot is working in a large scale environment. In this paper, we propose indoor re-localization solutions based on hierarchical Wi-Fi fingerprinting for an autonomous mobile robot. Hierarchical Wi-Fi fingerprinting requires less time for re-localizing the robot compared to laser-based MCL methods. Hierarchical Wi-Fi fingerprinting can immediately provide information to approximately localize which room the robot is in and the robot's approximate position after robot loses its position. The room-level localization is achieved by dendogram-based support vector machines (DSVM) and the robot's approximate position is estimated by Wi-Fi fingerprinting. After the robot's approximate position is calculated, more accurate robot position is attained by performing feature matching between laser measurements with the local 2D map. Finally, we propose the experiments in a practical indoor space. We use the laser-based generated SLAM map using adaptive Monte Carlo localization to compare with the proposed method. The experimental results show that using hierarchical Wi-Fi fingerprinting with laser-based localization can have good performance on robot re-localization.