A PHD FILTER BASED RELATIVE LOCALIZATION FILTER FOR ROBOTIC SWARMS

Abstract

In this thesis, we present a Probability Hypothesis Density (PHD) filter based relative localization system for robotic swarms. The system is designed to use only local information collected by onboard lidar and camera sensors to identify and track other swarm members within proximity. The multi-sensor setup of the system accounts for the inability of single sensors to provide enough information for the simultaneous identification of teammates and estimation of their position. However, it also requires the implementation of sensor fusion techniques that do not employ complex computer vision or recognition algorithms, due to robots’ limited computational capabilities. The use of the PHD filter is fostered by its inherent multi-sensor setup. Moreover, it aligns well with the overall goal of this localization system and swarm setup that does not require the association of a unique identifier to each team member. The system was tested on a team of four robots. This thesis content was accepted to DARS-SWARM 2021 conference [1].