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Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots - Evaluation Dataset Open Access

We consider the use of low-budget omnidirectional platforms for 3D mapping and self-localisation. These robots specifically permit rotational motion in the plane around a central axis, with negligible displacement. In addition, low resolution and compressed imagery, typical of the platform used, results in high level of image noise (σ ∽ 10). We observe highly sparse image feature matches over narrow inter-image baselines. This particular configuration poses a challenge for epipolar geometry extraction and accurate 3D point triangulation, upon which a standard structure from motion formulation is based. We propose a novel technique for both feature filtering and tracking that solves these problems, via a novel approach to the management of feature bundles. Noisy matches are efficiently trimmed, and the scarcity of the remaining image features is adequately overcome, generating densely populated maps of highly accurate and robust 3D image features. The effectiveness of the approach is demonstrated under a variety of scenarios in experiments conducted with low-budget commercial robots. This is the evaluation data set used in the work and comprises the images and associated ground truth measurements used for the results within the paper (doi: 10.1109/ICIP.2015.7351744).

Descriptions

Resource type

Dataset

Contributors

Creator: Breckon, Toby P. ¹
Creator: Cavestany, Pedro ¹
Contact person: Breckon, Toby P. ¹

¹ Durham University, UK

Funder

Higher Education Funding Council for England
Science and Technology Regional Office, Séneca Foundation, Murcia (Spain).

Research methods

Other description

Data set used in research paper doi:10.1109/ICIP.2015.7351744

Keyword

Structure from motion, Mobile robot, Omnidirectional, Noise, Feature filtering

Subject

Computer science

Location

Durham

Language

English

Cited in

doi:10.1109/ICIP.2015.7351744

Identifier

doi:10.15128/3n203z084
ark:/32150/3n203z084

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

September 2015

File Details

Depositor

T. Breckon

Date Uploaded

21 January 2016, 18:01:42

Date Modified

17 October 2016, 15:10:17

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Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 13647039

Last modified: 2016:01:21 18:11:53+00:00

Filename: cavestany15robot-sfm.zip

Original checksum: cb51ac41d86b8d81dfe80e7d706aa2b7