Map Building by Sequential Estimation of Inter-feature Distances

Authors:
Atsushi Ueta;Takehisa Yairi;Hirofumi Kanazaki;Kazuo Machida
Affiliations:
The University of Tokyo, Tokyo, Japan 153-8904;The University of Tokyo, Tokyo, Japan 153-8904;The University of Tokyo, Tokyo, Japan 153-8904;The University of Tokyo, Tokyo, Japan 153-8904
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

This paper proposes an alternative solution to a mapping problem in two different cases; when bearing measurement to features (landmarks) and odometry are measured and when local position of 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 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.