Relative Constrained SLAM for Robot Navigation

Abstract

This paper presents a relative-constrained SLAM formulation where partial a priori landmark information is built into the SLAM problem. Incorporating a priori relative constraints is motivated by the desire to avoid drawbacks of global constraints and to reduce uncertainty in the overall map and pose estimates. First, a Relative Deterministic-Constrained SLAM (RDC-SLAM) method is presented, where a Lagrange multiplier term is added to the cost function of the standard graph-based SLAM method, realizing a new deterministic-constrained least squares solution. Next, this method is extended to incorporate probabilistic constraints and is solved using chance-constrained optimization for a more robust least square solution, leading to Relative Probabilistic-Constrained SLAM (RPC-SLAM). Both RDC-SLAM and RPC-SLAM are tested within a Monte-Carlo framework using a 2D dataset. It is shown that the RPC-SLAM framework outperforms the other methods considered when landmark initialization is poor.