Map building without localization by estimation of inter-feature distances

Authors:
Atsushi Ueta;Takehisa Yairi;Hirofumi Kanazaki;Kazuo Machida
Affiliations:
(Correspd. Tel./Fax: +81 3 5452 5219/ E-mail: ueta@space.rcast.u-tokyo.ac.jp) School of Engineering, University of Tokyo, Tokyo, Japan;Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan;Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan;Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
Venue:
Intelligent Data Analysis - Artificial Intelligence
Year:
2010

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Abstract

This paper proposes an alternative solution to a mapping problem in two different cases; when bearing measurements to features (landmarks) and odometry are measured and when bearing and range measurements to features are measured. Our approach named M-SEIFD (Mapping by Sequential Estimation of Inter-Feature Distances) first estimates inter-feature distances, then finds global position of all the features by enhanced multi-dimensional scaling (MDS). M-SEIFD is different from the conventional SLAM methods based on Bayesian filtering in that robot self-localization is not compulsory and that M-SEIFD is able to utilize prior information about relative distances among features directly. We show that M-SEIFD is able to achieve a decent map of features both in simulation and in real-world environment with a mobile robot.