A Feasible Registration Method for Underwater SLAM

Simultaneous localization and mapping (SLAM) algorithm could make up for the disadvantages of underwater navigation methods based on priori maps, hence makes underwater vehicles truly autonomous. A modified data association method is proposed to lighten the systemic computational burden and improve data association process. The method makes use of a two-step filtration to solve the ambiguities arisen by multiple observations falling into the validation gate of a single feature or an observation lying in the overlapping region of gates of multiple features. Simulation experiments results show that the method could achieve a satisfactory association result with O(mn) computation complexity even when dead-reckoning error is quite large, thus suitable to on-line data association for underwater SLAM implementations.

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