10.15128/3n203z084
Breckon, Toby P.
Durham University, UK
Cavestany, Pedro
Durham University, UK
Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots - Evaluation Dataset
Durham University
2016
Durham University
Durham University
Higher Education Funding Council for England
Science and Technology Regional Office, Séneca Foundation, Murcia (Spain).
Breckon, Toby P.
Durham University, UK
Computer science
Structure from motion, Mobile robot, Omnidirectional, Noise, Feature filtering
2016-01-21
Dataset
13647039
application/zip
Creative Commons Attribution 4.0 International (CC BY)
ark:/32150/3n203z084
10.1109/ICIP.2015.7351744
Data set used in research paper doi:10.1109/ICIP.2015.7351744
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).