Bayesian multi-hypothesis scan matching

This paper proposes a multi-hypothesis solution to the simplified problem of simultaneous localization and mapping (SLAM) that arises when only two measurement frames are available. The proposed solution calculates hypothesis probabilities according to modeling based on standard multitarget tracking (MTT). State estimation is carried out by a hybrid technique consisting of extended Kalman filtering (EKF) and natural gradient (NG) optimization. The search for promising candidate hypotheses is carried out by Bron & Kerbosh´ clique detection algorithm. Both Monte-Carlo simulations and implementation on real-world sonar data show that the proposed approach has desirable robustness properties.