Dictionary-Based Map Compression for Sparse Feature Maps

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概要

• 論文の詳細を見る

• Obtaining a compact representation of a large-size feature map built by mapper robots is a critical issue in recent mobile robotics. 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 the proposal of the dictionary-based map compression approach. A map compression system is presented by employing RANSAC map matching and sparse coding as building blocks. The effectiveness levels of the proposed techniques is investigated in terms of map compression ratio, compression speed, the retrieval performance of compressed/decompressed maps, as well as applications to the Kolmogorov complexity.

• 2012-02-01

著者

• Tanaka Kanji

  Faculty Of Department Of Mechanical Science And Engineering Graduate School Of Engineering Kyushu Un

• Tanaka Kanji

  Faculty Of Engineering University Of Fukui

• Nagasaka Tomomi

  Faculty Of Engineering University Of Fukui

関連論文

• A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor(Artificial Intelligence and Cognitive Science)
• Location-Driven Image Retrieval for Images Collected by a Mobile Robot
• Towards Real-Time Global Localization in Dynamic Unstructured Environments
• LSH-RANSAC : Incremental Matching of Large-Size Maps
• Dictionary-Based Map Compression for Sparse Feature Maps

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