A Probabilistic Method of Bearing-only Localization by Using Omnidirectional Vision Signal Processing

Self-localization is the most necessary foundation of mobile robot´s navigation system. Omnidirectional vision system has been widely used in mobile robot. However, the accuracy of vision-based methods is not enough for many applications. In this paper, based on bearing-only localization, a probabilistic method is presented to accurately estimate mobile robot´s position making use of omnidirectional vision signal processing. Firstly, landmarks and bearings are detected by omnidirectional vision signals. Then, a probabilistic mapping composed of coordinates´ weight is constructed. The weight indicates the possibility that the robot´s location is the coordinate of the point. Finally, Monte Carlo Localization is used to merge this probabilistic mapping with odometer-based prediction model. Experiments in several scenarios show that our method using omnidirectional vision signal processing is robust and accurate for mobile robot localization.