ICRA 2011 Paper Abstract


Paper TuP210.6

nagasaka, tomomi (univ. of fukui), Tanaka, Kanji (Fukui University)

Dictionary-Based Map Compression for Sparse Feature Maps

Scheduled for presentation during the Regular Sessions "Localization and Mapping IV" (TuP210), Tuesday, May 10, 2011, 16:40−16:55, Room 5E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Localization, Mapping, SLAM


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.



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