Leg detection and tracking for a mobile robot and based on a laser device, supervised learning and particle filtering

Abstract

People detection and tracking is an essential skill to obtain social and interactive robots. Computer vision has been widely used to solve this task but images are affected by noise and illumination changes. Laser range finder is robust against illumination changes so that it can bring useful information to carry out the detection and tracking. In fact, multisensor approaches are showing the best results. In this work, we present a new method to detect and track people using a laser range finder. Patterns of leg are learnt from 2d laser data using supervised learning. Unlike others leg detection approaches, people can be still or moving at the surroundings of the robot. The method of leg detection is used as observation model in a particle filter to track the motion of a person. Experiments in a real indoor environment have been carried out to validate the proposal.