Robot localization in nonsmooth environments: experiments with a new filtering technique

Considers the localization problem for a unicycle robot equipped with range finders and moving in environments with nonsmooth geometry, i.e., whose obstacle-free region, has a piecewise-linear boundary. Using the multi-hypothesis density filter, a multi-modal estimator based on the Bayesian framework, an innovative localization system is devised and implemented on the ATRV-Jr robot. Experiments illustrate the superior performance of the new filter with respect to the classical extended Kalman filter.