3D Spatial Information Restoration Based on G-ICP Approach with LiDAR and Camera Mounted on an Autonomous Surface Vehicle

Abstract

In this study, we proposed a 3D spatial information restoration approach using LiDAR and camera to improve the autonomy level of autonomous surface vehicles (ASVs). The preprocessing phase was designed for removal of inherence noise corresponding to data obtained from LiDAR and camera. Because RGB color information is sensitive to changes in illumination, a gamma correction and dark channel prior (DCP) approach was applied to minimize the rate of change of color information due to environmental factors. In addition, because using the LiDAR point cloud data (PCD) source as is would take a long time to process the data, and noise would reduce the accuracy of the data processing, we went through a preprocessing process to remove noise and outliers through filters. Then, the relative coordinate information between the LiDAR and camera was used to calibrate each data in advance, so that the RGB color information was projected on the filtered PCD. Subsequently, accumulated using generalized iterative closest point (G-ICP) approach in order to generate 3D spatial information. The field data obtained in an inland water environment was used to demonstrate the validity of the proposed approach, its results were described.