Improved Monte Carlo localization of autonomous robots through simultaneous estimation of motion model parameters

In recent years, there has been an increasing interest in autonomous navigation for lightweight flying robots. With regard to self-localization flying robots have several limitations compared to ground vehicles. Due to their limited payload flying vehicles possess only limited computational resources and are restricted to a few and lightweight sensors. Additionally the kinematics of flying robots is rather complex, which requires sophisticated motion models that are typically hard to calibrate. However, as the sensors provide only a limited amount of information, the motion models need to be highly accurate to reduce the potential increase of uncertainty caused by the movements of the vehicle. In this paper, we present a novel approach to simultaneous localization and estimation of motion model parameters and their adaptation in the context of a particle filter. To deal with sudden changes of parameters, our approach utilizes random sampling augmented by additional damping to avoid oscillations caused by the delayed detection of the changes. As we demonstrate in experiments with a real blimp, our method can deal with very sparse and imprecise sensor information and outperforms a standard Monte Carlo localization approach.