Using multi-hypothesis mapping to close loops in complex cyclic environments

This paper describes an off-line, iterative algorithm for simultaneous localization and mapping within large indoor environments. The proposed approach is based on the idea of separately tracking multiple robot pose hypotheses that are generated each time the robot closes a loop by revisiting an already mapped area. Each tracked pose hypothesis corresponds to a separate possible robot path and maintains a separate map. Loop identification is inherently pursued by a hybrid localization algorithm with global localization capabilities. During the first step of the algorithm, all recorded sensor information is sequentially processed, in order to create a set of possible robot paths and their corresponding maps. After all sensor input is processed, the history of all tracked hypotheses is validated in order to select the most probable robot path and the corresponding map. The second step of the algorithm utilizes an EM-like iterative procedure in order to close the loops identified by the first step, compute a correct robot pose history and produce the final map. Experimental results demonstrate that the proposed algorithm facilitates computation of correct maps regardless of the number of loops in the robot´s path