Map building by sequential estimation of inter-feature distances

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. © 2008 Springer Berlin Heidelberg.