Adaptive multi-model and entropy-based localization on context-aware robotic system

This paper presents an algorithm for robotic self-localization implemented on a context-aware robotic system. The self-localization algorithm is developed using the Particle Filtering (PF) method, with the enhancement from the technique of adaptive multi-model and entropy-based active sensing. The proposed solution is then utilized in the scenarios of robotic self-localization on a mobile robot platform. The feasibility and effectiveness of the adaptive multi-model and entropy based self-localization method is demonstrated in the experimental results.