Open AccessMultilayer Matching SLAM for Large-Scale and Spacious Environments
Author(s) -
Jingchuan Wang,
Liu Li,
Zhe Liu,
Weidong Chen
Publication year - 2015
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/61240
Subject(s) - computer science , maxima and minima , matching (statistics) , simultaneous localization and mapping , scale (ratio) , data association , computational complexity theory , blossom algorithm , robot , fisher information , artificial intelligence , decomposition , algorithm , matrix (chemical analysis) , mobile robot , machine learning , mathematics , quantum mechanics , probabilistic logic , biology , materials science , composite material , mathematical analysis , ecology , statistics , physics
In large-scale and spacious environments, keeping a reliable data association and reducing computational complexity are challenges for the implementation of Simultaneous Localization and Mapping (SLAM). Focused on these problems, a multilayer-matching-based incremental SLAM algorithm is proposed in this article. In this algorithm, SLAM is simplified as a problem composed of a least-square-based optimization problem and data association. Then, it is solved in two steps. Firstly, a multilayer matching method is applied to deal with the data-association problem. Both matching between observation and local map and matching between different local maps are carried out. The uncertainty of the results-matching is described by the Fisher information matrix. Secondly, the robot pose is optimized through an incremental QR decomposition method. This algorithm effectively avoids the local minima caused by the limited observation information, and can build a consistent map of the environment. Meanwhile, the characters (hierarchical and incremental) of the proposed algorithm ensure low computational complexity. Experiments on simulation environments and two kinds of real environments with different sparse features verify that the algorithm is applicable for real-time application in large-scale and spacious environments