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

Jose Martinez Carranza, Andrew Calway, Efficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurement. British Machine Vision Conference. September 2009. PDF, 6988 Kbytes. External information

Abstract

Point based visual SLAM suffers from a trade off between map density and computa- tional efi??ciency. With too few mapped points, tracking range is restricted and resistance to occlusion is reduced, whilst expanding the map to give dense representation signii??- 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 signii??cantly reduced computational cost. Results of real-time experiments with a hand-held camera demonstrate the effectiveness of the approach.

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