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Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Örebro University, School of Science and Technology, Örebro University, Sweden. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
GIM Ltd. Espoo Finland.
2016 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967Article in journal (Refereed) Epub ahead of print
Abstract [en]

Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

Place, publisher, year, edition, pages
John Wiley & Sons, 2016.
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
URN: urn:nbn:se:oru:diva-53368DOI: 10.1002/rob.21657OAI: oai:DiVA.org:oru-53368DiVA: diva2:1044255
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2016-11-04Bibliographically approved

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Stoyanov, Todor
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School of Science and Technology, Örebro University, Sweden
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