Abstract

This paper describes decoupled stochastic mapping (DSM), a new computationally efficient approach to large-scale concurrent mapping and localization (CML). DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the number of submaps. The approach is demonstrated via simulations and experiments. Simulation results are presented for the case of an autonomous underwater vehicle (AUV) navigating in an unknown environments with 110 and 1200 features using simulated observations of point features by a forward look sonar. Empirical tests are used to examine the consistency of the error bounds calculated by the different methods. Experimental results are also presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 m testing tank.

Citations