Robust 3D reconstruction using LiDAR and N - visual image

Abstract

3D image reconstruction is desirable in many applications such as city planning, cartography and many vision applications. The accuracy of the 3D reconstruction plays a vital role in many real world applications. We introduce a method which uses one LiDAR image and N conventional visual images to reduce the error and to build a robust registration for 3D reconstruction. In this method we used lines as features in both the LiDAR and visual images. Our proposed system consists of two steps. In the first step, we extract lines from the LiDAR and visual images using Hough transform. In the second step, we estimate the camera matrices using a search algorithm combined with the fundamental matrices for the visual cameras. We demonstrate our method on a synthetic model which is an idealized representation of an urban environment.

Toward 3D Reconstruction of Outdoor Scenes Using an MMW Radar and a Monocular Vision Sensor

“…LiDAR provides a large number of accurate 3D points from a narrow field of view. Other examples could be found in [ 23 , 24 ]. However, the alignment of the large amount of data requires heavy processing algorithms that can be memory and time consuming.…”