Map building without localization by estimation of inter-feature distances

Issue title: Artificial Intelligence

Guest editors: Tu Bao Hox, Zhi-Hua Zhouy and Hiroshi Motodaz

Article type: Research Article

Authors: Ueta, Atsushi^{a; *} | Yairi, Takehisa^b | Kanazaki, Hirofumi^b | Machida, Kazuo^b

Affiliations: [a] School of Engineering, University of Tokyo, Tokyo, Japan | [b] Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan | [x] Japan Advanced Institute of Science and Technology, Ishikawa, Japan | [y] Nanjing University, Nanjing, China | [z] Osaka University and AFOSR/AOARD, Osaka, Japan

Correspondence: [*] Corresponding author: Atsushi Ueta, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8904 Japan. Tel./Fax: +81 3 5452 5219; E-mail: [email protected].

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.

Keywords: Mapping, mobile robot, multi-dimensional scaling

DOI: 10.3233/IDA-2010-0435

Journal: Intelligent Data Analysis, vol. 14, no. 4, pp. 515-529, 2010