Efficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurement

Jose Martinez Carranza, Andrew Calway

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

6 Citations (Scopus)

Abstract

Point based visual SLAM suffers from a trade off between map density and computa- tional efficiency. With too few mapped points, tracking range is restricted and resistance to occlusion is reduced, whilst expanding the map to give dense representation signifi- cantly increases computation. We address this by introducing higher order structure into the map using planar features. The parameterisation of structure allows frame by frame adaptation of measurements according to visibility criteria, increasing the map density without increasing computational load. This facilitates robust camera tracking over wide changes in viewpoint at significantly reduced computational cost. Results of real-time experiments with a hand-held camera demonstrate the effectiveness of the approach.
Translated title of the contributionEfficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurement
Original languageEnglish
Title of host publicationBritish Machine Vision Conference
PublisherBritish Machine Vision Association
Publication statusPublished - 2009

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: British Machine Vision Conference
Other identifier: 2001079

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