Multi-modal people tracking on a mobile companion robot

Abstract

People detection and tracking are key aspects in current research on mobile robots. While plenty of research is focused on pedestrian tracking in public areas, fewer work exists on practical people tracking in home environments with non static cameras. This paper presents a real-time people tracking system for mobile robots that applies multiple asynchronous detection modules and an efficient Kalman filter. It allows for upright pose — and under restrictions, sitting pose — people tracking in home environments. We evaluate the performance of the tracking system using different detection modalities on newly collected indoor data sets. These data sets are made publicly available for comparison and benchmarking.