Dictionary-Based Compressive SLAM

DOI Web Site 2 Citations 9 References Open Access
  • Kanji Tanaka
    Department of Human & Artifitial Intelligence Systems, Faculty of Engineering, Univ. of Fukui
  • Tomomi Nagasaka
    Department of Human & Artifitial Intelligence Systems, Faculty of Engineering, Univ. of Fukui

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Description

Obtaining a compact representation of a large-size datapoint 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 technique. Experiments show promising results in terms of compression speed, compactness of data and structure, as well as an application to the compression distance.

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