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Low-viewpoint forest depth dataset for sparse rover swarms

Low-viewpoint forest depth dataset for sparse rover swarms
Low-viewpoint forest depth dataset for sparse rover swarms
Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.
8035-8040
IEEE
Niu, Chaoyue
ab5e47f0-384f-411b-ae79-d29c8aea681b
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97
Niu, Chaoyue
ab5e47f0-384f-411b-ae79-d29c8aea681b
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97

Niu, Chaoyue, Tarapore, Danesh and Zauner, Klaus-Peter (2020) Low-viewpoint forest depth dataset for sparse rover swarms. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. pp. 8035-8040 .

Record type: Conference or Workshop Item (Paper)

Abstract

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.

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Published date: 25 October 2020
Venue - Dates: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Caesars Forum, Las Vegas, United States, 2020-10-25 - 2020-10-29

Identifiers

Local EPrints ID: 445360
URI: http://eprints.soton.ac.uk/id/eprint/445360
PURE UUID: dbadcb38-e5cb-4924-aa6e-38d389ee2837
ORCID for Chaoyue Niu: ORCID iD orcid.org/0000-0001-7626-0317
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 03 Dec 2020 17:35
Last modified: 11 Dec 2020 17:30

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Contributors

Author: Chaoyue Niu ORCID iD
Author: Danesh Tarapore ORCID iD
Author: Klaus-Peter Zauner

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