Cooperative Range-Only SLAM based on Rao-Blackwellized Particle Filter

In this paper, a new cooperative range-only simultaneous localization and mapping (RO-SLAM) algorithm is proposed, based on the Rao-Blackwellized particle filter (RBPF). The proposed cooperative approach, which measures ranges between any pair of two nodes cooperatively for the localization, has several advantages over the conventional RBPF based RO-SLAM algorithm. Firstly, the particle distribution with the importance weight can be quickly converged to the Gaussian distribution, which yields a fast convergence with the reduced computational burden. Secondly, the proposed algorithm using the inter-node measurements can yield improved localization accuracy. Thirdly, the accuracy is not affected by odometry error unlike the conventional RO-SLAM algorithm because the map estimation is done without moving a robot, and thus the accuracy level is maintained regardless of the sensor network size. These advantages of the proposed algorithm are verified through several real experiments.