Particle Filtering-based tracking and localization on context-aware robotic system

This paper develops the algorithm of human tracking and localization implemented on a context-aware robotic platform. The tracking and localization algorithm is developed using the Particle Filtering (PF) method, enhanced by the adaptive multi-model techniques and the entropy-based active sensing. The proposed solution is then utilized for human tracking and localization on a mobile robot platform. The feasibility and effectiveness of the entropy and multi-model based particle filtering method is demonstrated in the experimental results.