6D Relocalisation for RGBD Cameras Using Synthetic View Regression

Andrew P. Gee, Walterio Mayol-Cuevas, 6D Relocalisation for RGBD Cameras Using Synthetic View Regression. Proceedings of the British Machine Vision Conference (BMVC). September 2012. PDF, 2305 Kbytes.


With the advent of real-time dense scene reconstruction from handheld cameras, one key aspect to enable robust operation is the ability to relocalise in a previously mapped environment or after loss of measurement. Tasks such as operating on a workspace, where moving objects and occlusions are likely, require a recovery competence in or- der to be useful. For RGBD cameras, this must also include the ability to relocalise in areas with reduced visual texture. This paper describes a method for relocalisation of a freely moving RGBD camera in small workspaces. The approach combines both 2D image and 3D depth information to estimate the full 6D camera pose. The method uses a general regression over a set of synthetic views distributed throughout an informed es- timate of possible camera viewpoints. The resulting relocalisation is accurate and works faster than framerate and the systema??s performance is demonstrated through a compari- son against visual and geometric feature matching relocalisation techniques on sequences with moving objects and minimal texture.

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