Dynamic Object Detection in Range Data Using Spatiotemporal Normals

Falque, R; LeGentil, C; Sukkar, F

Dynamic Object Detection in Range Data Using Spatiotemporal Normals

Falque, R LeGentil, C Sukkar, F

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Publisher:: Australasian Conference on Robotics and Automation
Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2023, pp. 190-186
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Falque, R
dc.contributor.author	LeGentil, C
dc.contributor.author	Sukkar, F
dc.date	2023-12-02
dc.date.accessioned	2024-05-30T20:44:41Z
dc.date.available	2024-05-30T20:44:41Z
dc.date.issued	2023-01-01
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2023, pp. 190-186
dc.identifier.isbn	978-0-6455655-2-2
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/179357
dc.description.abstract	On the journey to enable robots to interact with the real world where humans animals and unpredictable elements are acting as independent agents it is crucial for robots to have the capability to detect dynamic objects In this paper we argue that the detection of dynamic objects can be solved by computing the spatiotemporal normals of a point cloud In our experiments we demonstrate that this simple method can be used robustly for LiDAR and depth cameras with performances similar to the state of the art while offering a significantly simpler method 2023 Australasian Robotics and Automation Association All rights reserved
dc.language	en
dc.publisher	Australasian Conference on Robotics and Automation
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA
dc.relation.ispartof	Australasian Conference on Robotics and Automation
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Dynamic Object Detection in Range Data Using Spatiotemporal Normals
dc.type	Conference Proceeding
utslib.location.activity	Sydney, Australia
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2024-05-30T20:44:38Z
pubs.finish-date	2023-12-04
pubs.place-of-publication	Australia
pubs.publication-status	Published
pubs.start-date	2023-12-02
dc.location	Australia

Abstract:

On the journey to enable robots to interact with the real world where humans animals and unpredictable elements are acting as independent agents it is crucial for robots to have the capability to detect dynamic objects In this paper we argue that the detection of dynamic objects can be solved by computing the spatiotemporal normals of a point cloud In our experiments we demonstrate that this simple method can be used robustly for LiDAR and depth cameras with performances similar to the state of the art while offering a significantly simpler method 2023 Australasian Robotics and Automation Association All rights reserved

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http://hdl.handle.net/10453/179357