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A Simultaneous Localization and Mapping Algorithm in Complex Environments: SLASEM

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Abstract:

In this paper we present an algorithm for the application of simultaneous localization and mapping in complex environments. Instead of building a grid map or building a feature map with a small number of the obstacles' geometric parameters, the proposed algorithm builds a sampled environment map (SEM) to represent a complex environment with a set of environment samples. To overcome the lack of one-toone correspondence between environment samples and raw observations, the signed orthogonal distance function is proposed to be used as the observation model. A method considering geometric constraints is presented to remove redundant environment samples from the SEM. We also present a method to improve the SEM's topological consistency by using corner constraints. The proposed algorithm has been verified in a simulation and an indoor experiment. The results show that the algorithm can localize the robot and build a complex map effectively.

Keywords: KALMAN FILTER; LOCALIZATION; MAPPING; MOBILE ROBOTS; SLAM

Document Type: Research Article

DOI: http://dx.doi.org/10.1163/016918611X563373

Affiliations: 1: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China, Graduate School of the Chinese Academy of Sciences, Beijing 100039, P.R. China 2: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China, Department of Robotics, Ritsumeikan University, Kusatsu-Shi 525-8577, Japan;, Email: shugen@se.ritsumei.ac.jp 3: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China

Publication date: January 1, 2011

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