Obtaining a compact representation of a large size feature map built by mapper robots is a critical issue in the context of lightweight information sharing as well as Kolmogorov complexity. This map compression problem is explored from a novel perspective of dictionary-based data compression techniques in the paper. The primary contribution of the paper is proposal of the dictionary-based map compression approach. A map compression system is developed using RANSAC map matching and sparse coding as building blocks. Experiments show promising results in terms of map compression ratio, compression speed as well as the retrieval performance of compressed/decompressed maps.

Obtaining a compact representation of a largesize pointset map built by mapper robots is a critical issue for recent SLAM applications. This ¿map compression¿ problem is explored from a novel perspective of dictionary-based map compression in the paper. The primary contribution of the paper is proposal of an incremental scheme for simultaneous mapping and map-compression applications. An incremental map compressor is presented by employing a modified RANSAC map-matching scheme as well as the compact projection visual search. Experiments show promising results in terms of compression speed, compactness of data and structure, as well as an application to the compression distance.