Abstract:

A novel simultaneous localization and mapping (SLAM) data association approach based on fuzzy logic is proposed. It firstly calculates the error ellipses of the feature observation and estimation, and fuzzes up the normalized residue and percentage of the two ellipses’ overlap. And then these informations are used as the input of fuzzy system variables, in the meanwhile the data association result is defined as a fuzzy output variable. The new algorithm carries out the fuzzy reasoning by fusioning the feature information through the fuzzy rules, and the association result is obtained in the end. This approach describes the uncertainty and fuzziness in the data association validly, and it has the ability to deal with multiple association hypothesis. Further more, it can reduce the false association when the distance between true observation and estimation is close, and it can avoid discarding the true association when the distance is far. The simulation results demonstrate that the proposed approach is of better anti-interference ability and robustness, and it provides a new way for data association of the SLAM problem.