Abstract A mobile robot needs to perceive the motions of external objects to perform tasks successfully in a dynamic environment. We propose a set of algorithms for multiple motion tracking from a mobile robot equipped with a monocular camera and a laser rangefinder. The key challenges are 1. to compensate the ego-motion of the robot for external motion detection, and 2. to cope with transient and structural noise for robust motion tracking. In our algorithms, the robot ego-motion is directly estimated using corresponding feature sets in two consecutive images, and the position and velocity of a moving object is estimated in image space using multiple particle filters. The estimates are fused with the depth information from the laser rangefinder to estimate the partial 3D position. The proposed algorithms have been tested with various configurations in outdoor environments. The algorithms were deployed on three different platforms; it was shown that various type of ego-motion were successfully eliminated and the particle filters were able to track motions robustly. The real-time capability of the tracking algorithm was demonstrated by integrating it into a robot control loop.
To track humans in a moving environment, Jung and Sukhatme REF proposed a probabilistic approach for moving object detection when using a single camera on a mobile robot in outdoor environments.
Real-time Motion Tracking from a Mobile Robot
TO APPEAR IN INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS SPECIAL ISSUE ON PEOPLE DETECTION AND TRACKING
Psychology Computer Science