Data association using empty convex polygonal regions in EKF-SLAM

This paper proposes a new framework for data association to solve the problem of SLAM. The proposed framework has specific relevance to range scanner based EKF-SLAM. The resulting data representation enables semantic reasoning on a spatial level which reduces the misassociation of closely spaced data from different spatial configurations through the use of convex polygons to represent data from similar spatial configurations. The data representation is especially effective for association when revisiting previously mapped regions efficiently. The spatial data representation also builds an occupancy grid for the entire map. We also provide a means of clustering range scan data using an adaptive threshold to be able to divide data at various ranges into clusters and dense data clustering to get more accurate data.