Compressive SLAM-

Abstract
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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.
Repetitive patterns discovered. Shown in the figure are all the repetitive patterns discovered during a dictionary-based map compression task. Currently, the pointsets in the dictionary are not compressed. The pointsets could be further compressed exploiting the redundancy, for example, by employing techniques from point-based geometry.
Incremental map compression. The input is a sequence of submaps built by mapper robots during a SLAM task (top figure, 24 submaps, each is distinguished by different colors). A set of datapoints are compactly represented in the form of compression trajectory, a sequence of transformed datapoints (middle figure, random 100 examples of compression trajectories). The incremental map compression is a process of updating a set of compression trajectories by incorporating latest submap (bottom figures, respectively corresponding to the 1st, 2nd, ..., 24th update).
The incremental compression scheme. The core of the scheme is easy to implement (in tens of lines of C code) if an existing module for map-matching as well as visual search is reused.
Compression trajectory. A compression trajectory represents a sequence of transformed datapoints. Each point on the trajectory is an approximation of an original datapoint. The approximation error is smaller than a threshold and free from error accumulation.
A decompression result. Top: the decompressed map superimposed on the original map. Bottom: accumulative frequency of spatial error [m].




Acknowledgement This work is This work was partially supported by MECSST Grant in-Aid for Young Scientists (B) (23700229), by KURATA grants and by TATEISI Science And Technology Foundation.




Members Tanaka Kanji, Ikeda Kouichirou, Nagasaka Tomomi, Kondo Kensuke