Accelerated Volumetric Next-Best-View Planning in 3D Mapping

Abstract: The Next-Best-View (NBV) problem plays an important part in automatic 3D object reconstruction and exploration applications. This thesis presents a novel approach of ray-casting in Occupancy Grid Maps (OGM) in the context of solving the NBV problem in a 3D-exploration setting. The proposed approach utilizes the structure of an octree-based OGM to perform calculations of potential information gain. The computations are significantly faster than current methods, without decreasing mapping quality. Performance, both in terms of mapping quality, coverage and computational complexity, is experimentally verified through a comparison with existing state-of-the-art methods using high-resolution point cloud data generated using time-of-flight laser range scanners. Current methods for viewpoint ranking focus either heavily on mapping performance or computation speed. The results presented in this thesis indicate that the proposed method is able to achieve a mapping performance similar to the performance-oriented approaches while maintaining the same low computation speed as more approximative methods.