Robust linear pose graph-based SLAM

This paper addresses a robust and efficient solution to eliminate false loop-closures in a pose-graph linear SLAM problem. Linear SLAM was recently demonstrated based on submap joining techniques in which a nonlinear coordinate transformation was performed separately out of the optimization loop, resulting in a convex optimization problem. This however introduces added complexities in dealing with false loop-closures, which mostly stems from two factors: (a) the limited local observations in map-joining stages and (b) the non block-diagonal nature of the information matrix of each submap. To address these problems, we propose a Robust Linear SLAM by (a) developing a delayed optimization for outlier candidates and (b) utilizing a Schur complement to efficiently eliminate corrupted information block. Based on this new strategy, we prove that the spread of outlier information does not compromise the optimization performance of inliers and can be fully filtered out from the corrupted information matrix. Experimental results based on public synthetic and real-world datasets in 2D and 3D environments show that this robust approach can cope with the incorrect loop-closures robustly and effectively.