Dimensionless Monocular SLAM

Abstract. It has recently been demonstrated that the fundamental computer vision problem of structure from motion with a single camera can be tackled using the sequential, probabilistic methodology of monocular SLAM (Simultaneous Localisation and Mapping). A key part of this approach is to use the priors available on camera motion and scene structure to aid robust real-time tracking and ultimately enable metric motion and scene reconstruction. In particular, a scene object of known size is normally used to initialise tracking. In this paper we show that real-time monocular SLAM can be initialised with no prior knowledge of scene objects within the context of a powerful new dimensionless understanding and parameterisation of the problem. When a single camera moves through a scene with no extra sensing, the scale of the whole motion and map is not observable, but we show that up-to-scale quantities can be robustly estimated. Further we describe how the monocular SLAM state vector can be ...

• Type: Conference
• Year: 2007
• Where: IBPRIA
• Authors: Javier Civera, Andrew J. Davison, J. M. M. Montiel
• Keywords: Camera Motion, IBPRIA 2007, Image, Image Analysis, Monocular Slam, Scene Objects