A Novel Fusion Method of 3D Point Cloud and 2D Images for 3D Environment Reconstruction

Combined 3D laser scanner and monocular camera is an important way to reconstruct 3D environment. This paper presents a new fusion scheme which takes more comprehensive use of heterogeneous data from different sensors. First, we extract the 3D structure information from sequence images to aid initial registration which provides a sufficiently accurate pose estimation for an ICP algorithm to perform the fine alignment. Second, extracting points from sequence images by dense reconstruction, registering the heterogeneous data that can supplement the details of model and solve the problem of ambiguity of images. The efficiency of the presented method has been tested on simulation software we programmed which simulates the process of heterogeneous data acquired and model reconstruction. The results show that the initial registration can acquired an accuracy and stable alignment for ICP and reconstruct more accuracy model, and this method does not need joint calibration of laser scanner and camera or manual intervention, and has better adaptability.