OctoMap: an efficient probabilistic 3D mapping framework based on octrees View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-02-07

AUTHORS

Armin Hornung, Kai M. Wurm, Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard

ABSTRACT

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum. More... »

PAGES

189-206

References to SciGraph publications

Journal

TITLE

Autonomous Robots

ISSUE

3

VOLUME

34

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10514-012-9321-0

    DOI

    http://dx.doi.org/10.1007/s10514-012-9321-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023420089


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