Self-localization based on a short-term memory of bearings and odometry

In this paper we introduce a localization method which is based on a memory of horizontal bearings to landmarks and odometry. The approach is perfectly suited for mobile robots equipped with a camera because bearings can be extracted from images with high accuracy. In contrast to existing approaches, our method does not need any internal representation of the robot´s position which is updated by alternating motion and sensor updates. In our approach the location is calculated by applying constraints on the robot´s position which are derived from the observations and performed actions that are stored in a short-term memory. We give a detailed description of the method and analyze the properties of different observation selection mechanisms. Results of experiments done in simulation and conducted on a Sony Aibo robot are presented in this paper demonstrating the precision of the method.