Real-Time Tracking of Moving Objects Using Particle Filters

Abstract

Mobile robots and vehicles are increasingly used in dynamic environments populated by humans and other moving objects and vehicles. In this context, tracking of surrounding moving objects is important for obstacle avoidance and motion planning. In this paper we present a method for detection and tracking of multiple moving objects using particle filters to estimate the object states, and sample based joint probabilistic data association filters to perform the assignment between the features detected in the input sensor data and filters. Filters management operations are required for appropriate integration of the currently perceived features. A real-time architecture, developed to implement the tracking system, is briefly described. Exper- imental results obtained with a laser range scanner will be presented demonstrating the feasibility and effectiveness of the presented methods.