We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots.

Cavestany, P. and Rodríguez, A.L. and Martínez-Barberá, H. and Breckon, T.P. (2015) 'Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots.', in 2015 IEEE International Conference on Image Processing, ICIP 2015, 27-30 September 2015, Quebec City, QC, Canada ; proceedings. New York, USA: IEEE, pp. 4927-4931.


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 (_ ∽ 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.

Item Type:Book chapter
Keywords:Structure from motion, Mobile robot, Omnidirectional, Noise, Feature filtering.
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Publisher statement:© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:28 October 2015
Date of first online publication:September 2015
Date first made open access:No date available

Save or Share this output

Look up in GoogleScholar