Robust tracking and mapping with a handheld RGB-D camera
In this paper, we propose a robust method for camera tracking and surface mapping using a handheld RGB-D camera which is effective in challenging situations such as fast camera motion or geometrically featureless scenes. The main contributions are threefold. First, we introduce a robust orientation estimation based on quaternion method for initial sparse estimation. By using visual feature points detection and matching, no prior or small movement assumption is required to estimate a rigid transformation between frames. Second, a weighted ICP (Iterative Closest Point) method for better rate of convergence in optimization and accuracy in resulting trajectory is proposed. While the conventional ICP fails when there is no 3D features in the scene, our approach achieves robustness by emphasizing the influence of points that contain more geometric information of the scene. Finally, we show quantitative results on an RGB-D benchmark dataset. The experiments on an RGB-D trajectory benchmark dataset demonstrate that our method is able to track camera pose accurately.