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Autonomous Robots
Erschienen in: Autonomous Robots 3/2014

01.10.2014

Feature based graph-SLAM in structured environments

verfasst von: P. de la Puente, D. Rodriguez-Losada

Erschienen in: Autonomous Robots | Ausgabe 3/2014

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Abstract

Introducing a priori knowledge about the latent structure of the environment in simultaneous localization and mapping (SLAM), can improve the quality and consistency results of its solutions. In this paper we describe and analyze a general framework for the detection, evaluation, incorporation and removal of structure constraints into a feature-based graph formulation of SLAM. We specifically show how including different kinds and levels of features in a hierarchical manner allows the system to easily discover new structure and why it makes more sense than other possible representations. The main algorithm in this framework follows an expectation maximization approach to iteratively infer the most probable structure and the most probable map. Experimental results show how this approach is suitable for the integration of a large variety of constraints and how our method can produce nice and consistent maps in regular environments.
Vorheriger Artikel Fast RGB-D people tracking for service robots
Nächster Artikel RMAP: a rectangular cuboid approximation framework for 3D environment mapping

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Metadaten
Titel
Feature based graph-SLAM in structured environments
verfasst von
P. de la Puente
D. Rodriguez-Losada
Publikationsdatum
01.10.2014
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3/2014
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-014-9386-z

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