Particle Attraction Localisation

Abstract — In this paper we present an original method for Bayesian localisation based on particle approximation. Our method overcomes a majority of problems inherent in previous Kalman filter and Bayesian approaches, including the recent Monte Carlo Localisation methods. The algorithm converges quickly to any desired precision. It does not overconverge in the case of highly accurate sensor data and thus does not require a mixture-based approach. Also, the algorithm recovers well from random repositioning. These benefits are not hindered by computation which can be performed in real time on low powered processors. Further, the algorithm is intuitive and easy to implement. This algorithm is evaluated in simulation and has been applied to our entrant in the Sony Four Legged League of RoboCup, where it has been tested over many hours of international competition. I