A decoupled approach for simultaneous stochastic mapping and mobile robot localization

This paper introduces a decoupled approach of concurrent mapping and localization for mobile robots. Its theoretical aspects rely on recent techniques for correct uncertainty handling using stochastic models: covariance intersection and unscented transform. Further, stochastic constraints are considered as a way to minimize map incoherence with respect to the real environment. Experimental results obtained from multisensory data acquired in a large real environment illustrate the performance of the proposed method.