Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description

D Chekhlov, ML Pupilli, WW Mayol-Cuevas, AD Calway

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

62 Citations (Scopus)

Abstract

Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. The key component is a novel feature description method which is both fast and capable of repeatable correspondence matching over a wide range of viewing angles and scales. This is achieved in real-time by using a SIFT-like spatial gradient descriptor in conjunction with efficient scale prediction and exemplar based feature representation. Results are presented illustrating robust realtime SLAM operation within an office environment.
Translated title of the contributionRobust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description
Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition, 17-22 June 2007
PublisherIEEE Computer Society
Pages1 - 7
Number of pages7
ISBN (Print)1424411807
DOIs
Publication statusPublished - Jun 2007

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: IEEE International Conference on Computer Vision and Pattern Recognition
Other identifier: 2000694

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