A New Sensor Model for Particle-Filter Based Localization in the Partially Unknown Environments

Localization is an important ability for a mobile robot. The probabilistic localization method becomes more popular because of the ability of representing the uncertainties of the sensor measurements and inaccuracy environments, robust solutions for a wide perspective of localization problem. The particle filter is one of the Bayesian-based methods. In this study, a new sensor model ((RSM)-S-2) is proposed and integrated to traditional particle filter to reduce the effects of outliers. The proposed approach is applied in the global and position tracking localization problems for a mobile robot in static and partially unknown experimental environments. Performance of the proposed method is compared with the performance of the traditional particle filter approaches as the several parameters of the system are varied. These analyses show that the proposed approach improves the localization success. Additionally, the proposed method is realized by using P3-DX mobile robot platform to solve the global and position tracking localization problems. In order to provide accurate navigation a simple orientation controller is designed. The experimental results are promising for the future works.