Real-Time Dense Monocular SLAM for Augmented Reality

Simultaneous localization and mapping (SLAM) via a monocular camera is a key enabling technique for many augmented reality (AR) applications. In this work, we present a monocular SLAM system which can provide real-time dense mapping even for challenging poorly-textured regions based on the piecewise planarity approximation. Specifically, our system consists of three modules. First, a tracking module based on the direct method [2] continuously estimates camera poses with respect to the scene. Second, a semi-dense mapping module takes the estimated camera pose as input and calculates depths of highly-textured pixels based on pixel matching and triangulation. Third, dense mapping module approximates textureless regions identified by a homogeneous-color region detector using piecewise plane models. The 3D piecewise planes are reconstructed via the proposed multi-plane segmentation and multi-plane fusion algorithms. Live experiments in a real AR demo with a hand-held camera demonstrate the effectiveness and efficiency of our method in practical scenario.