For mobile robots to operate in compliance with human presence, interpreting the impact of human activities and responding constructively is a challenging goal. In this paper, we propose a generative approach for enhancing robot mapping and mobility in the presence of humans through a joint, probabilistic treatment of static and dynamic characteristics of indoor environments. Human spatial activity is explicitly exploited for the purpose of passage detection and space occupancy prediction while effectively discarding false positive human detections using prior map information. In turn, this allows the execution of plan trajectories within unexplored areas by using human presence for resolving the uncertainty or ambiguity that is due to dynamic events. A series of experiments with an indoor robot navigating in close human proximity within a multi-floor building demonstrate the effectiveness of our approach in realistic conditions.
['Panagiotis Papadakis', 'Patrick Rives']
Binding human spatial interactions with mapping for enhanced mobility in dynamic environments