Human detection and tracking in an assistive living service robot through multimodal data fusion

Abstract

A new method is proposed for using a combination of measurements from a laser range finder and a depth camera in a data fusion process that benefits from each modality's strong side. The combination leads to a significantly improved performance of the human detection and tracking in comparison with what is achievable from the singular modalities. The useful information from both laser and depth camera is automatically extracted and combined in a Bayesian formulation that is estimated using a Markov Chain Monte Carlo (MCMC) sampling framework. The experiments show that this algorithm can track robustly multiple people in real world assistive robotics applications.