Fast initialization method for monocular SLAM based on indoor model

Abstract

The visual SLAM has been approved that it is extremely useful to obtain robust positions of camera for AR system. However, the traditional initialization of vSLAM will stop it being popular in AR system because the initialization approaches are too complex and inefficiently. For alleviating this limitation, we present a novel initializing method for monocular vSLAM system based on indoor model. In contrast to existing methods, the proposed method can even instantaneously initialize the vSLAM 3D map at the first frame. Given a single frame of the indoor image, the lines and vanishing points can be compiled, as well as the orientation map and a set of indoor model hypothesis, and the best fitting 3D indoor model can be estimated further. We use the indoor model to reliably initialize the vSLAM system. The experimental result on some public dataset proves the robustness and quickness of our initialization approach.