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|
|Title of host publication||British Machine Vision Conference|
|Publisher||British Machine Vision Association|
|Publication status||Published - 2009|
Bibliographical noteOther page information: -
Conference Proceedings/Title of Journal: British Machine Vision Conference
Other identifier: 2001079