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

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.