A new approach to improve the success ratio and localization duration of a particle filter based localization for mobile robots

In real world applications, it is important that mobile robots know their location to achieve goals correctly. The localization of the robot is difficult by using raw sensor data because of the noisy measurements from these sensors. To overcome this difficulty probabilistic localization algorithm approaches can be used. The Particle filter is one of the Bayesian-based methods. In this study, two new features incorporated into the particle filter approach. These features are: decreasing the size of sample space using compass data and a new sensor model. The proposed approach is applied in the localization problem of a mobile robot. Performance of the proposed algorithm is compared with the performance of traditional particle filter approach by changing several parameters of the system. These analyses emphasized that the proposed approach improved the localization performance of the system. The results are promising for the future studies on this subject.