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Novel Information Matrix Sparsification Approach for Practical Implementation of Simultaneous Localization and Mapping

Haiwei Dong, Jun Tang, Weidong Chen, Akinori Nagano, Zhiwei Luo

Year
2010
Citations
3

Abstract

Abstract Simultaneous localization and mapping (SLAM) is a fundamental issue in mobile robotics because it is the basis of higher-level tasks of robots. Recently, more and more research has been proposed that aims to enhance the efficiency of SLAM solutions from the viewpoint of the information matrix. This paper presents a novel, efficient SLAM approach by using the characters of the information matrix. Our approach eliminates many of the elements in the information matrix while maintaining the consistency. The large complex environment simulation, as well as outdoor car park experiment verifies the validity of our approach. The proposed sparsification method provides an efficient way to obtain a consistent estimation with provable upper bounds of sparsification errors. Keywords: MOBILE ROBOTSLAMINFORMATION MATRIXSPARSIFICATIONCONSISTENCY

Keywords

Simultaneous localization and mappingComputer scienceMobile robotRoboticsConsistency (knowledge bases)Matrix (chemical analysis)Artificial intelligenceBasis (linear algebra)RobotComputer vision

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