PTracking: Distributed Multi-Agent Multi-Object Tracking through Multi-Clustered Particle Filtering

The present work addresses the Distributed Multi-Agent Multi-Object Tracking problem where a team of robots has to perform a distributed position estimation of multiple moving objects. In complex scenarios, where mobile robots are involved, it is crucial to disseminate reliable beliefs in order to avoid the degradation of the global estimations. To this end, Distributed Particle Filters have been proven to be effective tools to model non-linear and dynamic processes in Multi-Robot Systems. We present therefore an asynchronous method for Distributed Particle Filtering based on Multi-Clustered Particle Filtering that uses a novel clustering technique to continuously keep track of a variable and unknown number of objects. A two-tiered architecture is proposed: a local estimation layer uses a Particle Filter to integrate local observations of multiple objects detected in the local frame, while a global estimation layer is used to perform a distributed estimation integrating information collected from the other robots. We carried out a quantitative evaluation demonstrating how our proposed approach has significantly better robustness to perception noise when using mobile sensors rather than fixed sensors.