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Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor - Zurich Open Repository and Archive


Brandli, C; Mantel, T; Hutter, M; Hopflinger, M; Berner, R; Siegwart, R; Delbruck, T (2014). Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor. Frontiers in Neuroscience, 7:275.

Abstract

Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm.

Abstract

Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm.

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3 citations in Web of Science®
7 citations in Scopus®
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33 downloads since deposited on 23 Feb 2015
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2014
Deposited On:23 Feb 2015 16:20
Last Modified:06 Jul 2017 09:55
Publisher:Frontiers Research Foundation
ISSN:1662-453X
Publisher DOI:https://doi.org/10.3389/fnins.2013.00275

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Licence: Creative Commons: Attribution 3.0 Unported (CC BY 3.0)

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