A pure probabilistic approach to range-only SLAM

Range-only SLAM (RO-SLAM) represents a difficult problem due to the inherent ambiguity of localizing either the robot or the beacons from distance measurements only. Most previous approaches to this problem employ non-probabilistic batch optimizations or delay the initialization of new beacons within a probabilistic filter until a good estimate is available. The contribution of this work is the formulation of RO-SLAM as an online Bayesian estimation process based on a Rao-Blackwellized particle filter. The conditional distribution for each beacon is initialized using an additional particle filter which, eventually, is transformed into an extended Kalman filter when the uncertainty becomes sufficiently small. This approach allows the introduction of new beacons without either delay or any special non-probabilistic processing. We validate our proposal with experiments for both simulated and real datasets.