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 contribution | Efficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurement |
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Original language | English |
Title of host publication | British Machine Vision Conference |
Publisher | British Machine Vision Association |
Publication status | Published - 2009 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: British Machine Vision Conference
Other identifier: 2001079