Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object

Abstract

An improved particle-filter algorithm is proposed to track a randomly moving object. The algorithm is implemented on a mobile robot equipped with a pan-tilt camera and 16 sonar sensors covering 360deg. Initially, the moving object is detected through a sequence of images taken by the stationary pan-tilt camera using the motion-detection algorithm. Then, the particle-filter-based tracking algorithm, which relies on information from multiple sensors, is utilized to track the moving object. The robot vision system and the control system are integrated effectively through the state variable representation. The object size deformation problem is taken care of by a variable particle-object size. When moving randomly, the object's position and velocity vary quickly and are hard to track. This results in serious sample impoverishment (all particles collapse to a single point within a few iterations) in the particle-filter algorithm. A new resampling algorithm is proposed to tackle sample impoverishment. The experimental results with the mobile robot show that the new algorithm can reduce sample impoverishment effectively. The mobile robot continuously follows the object with the help of the pan-tilt camera by keeping the object at the center of the image. The robot is capable of continuously tracking a human's random movement at walking rate