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

We consider 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 novel 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.