Incremental 3D Line Segments Extraction for Surface Reconstruction from Semi-dense SLAM

Abstract

Semi-dense SLAM systems have become popular in the last few years. They can produce much denser point clouds than sparse SLAM while being computationally efficient (using only CPU). In previous works, the surface of the viewed scene was reconstructed in real-time by combining sparse SLAM system and incremental surface reconstruction method. However, it is challenging to utilize the large scale point clouds of semi-dense SLAM for real-time surface reconstruction. In this thesis, in order to obtain meaningful surfaces and reduce the number of points used in surface reconstruction, we propose to simplify the point clouds generated by semi-dense SLAM using 3D line segments. Specifically, we present a novel incremental approach for real-time 3D line segments extraction. This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems, which take advantage of both image edge segments and depth maps. We first detect edge segments from keyframes. Then we search 3D line segments along the detected edge pixel chains by minimizing the fitting error on both image plane and depth plane. By incrementally clustering the detected line segments, the resulting 3D representation for the scene achieves a good balance between compactness and completeness. Our experimental results show that the 3D line segments generated by our method are highly accurate compared to other methods. With the reconstructed surfaces, we demonstrate that using the extracted 3D line segments greatly improves the quality of 3D surface compared to using the 3D points directly from SLAM systems.