Combining Local and Global Optical Flow for RGB-D Point Cloud Alignment

Abstract

3D scene reconstruction using RGB-D camera-based Simultaneous Localization and Mapping (SLAM) is constantly studied today. KinectFusion, GPU-based real-time 3D scene reconstruction framework, is mainly used for many other algorithms of RGB-D SLAM. One of the main limitation of KinectFusion depends only on geometric information in the camera pose estimation process. In this paper, we utilize both geometric and photometric information for point cloud alignment. To extract photometric information in color image, we combine local and global optical flow method, such as Lucas-Kanade and Horn-Schunck, respectively, and make not only dense but also robust to noise flow field. In experimental results, we show that our method can use dense and accurate photometric information.