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3D lidar reconstruction with probabilistic depth completion for robotic navigation

Abstract:

Safe motion planning in robotics requires planning into space which has been verified to be free of obstacles. However, obtaining such environment representations using lidars is challenging by virtue of the sparsity of their depth measurements. We present a learning-aided 3D lidar reconstruction framework that upsamples sparse lidar depth measurements with the aid of overlapping camera images so as to generate denser reconstructions with more definitively free space than can be achieved with...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IROS47612.2022.9981531

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/R026173/1
Publisher:
IEEE
Host title:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pages:
5339-5346,
Publication date:
2022-12-26
Acceptance date:
2022-06-30
Event title:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Event location:
Kyoto, Japan
Event website:
https://iros2022.org/
Event start date:
2022-10-23
Event end date:
2022-10-27
DOI:
EISSN:
2153-0866
ISSN:
2153-0858
EISBN:
9781665479271
ISBN:
9781665479288
Language:
English
Keywords:
Pubs id:
1274305
Local pid:
pubs:1274305
Deposit date:
2022-08-16

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