Efficient Next-Best-Scan Planning for Autonomous 3D Surface Reconstruction of Unknown Objects

Abstract

This work focuses on autonomous surface reconstruction of small-scale objects with a robot and a 3D sensor. The aim is a high-quality surface model allowing for robotic applications such as grasping and manipulation. Our approach comprises the generation of next-best-scan (NBS) candidates and selection criteria, error minimization between scan patches and termination criteria. NBS candidates are iteratively determined by a boundary detection and surface trend estimation of the acquired model. To account for both a fast and high-quality model acquisition, that candidate is selected as NBS, which maximizes a utility function that integrates an exploration and a mesh-quality component. The modeling and scan planning methods are evaluated on an industrial robot with a high-precision laser striper system. While performing the new laser scan, data are integrated on-the-fly into both, a triangle mesh and a probabilistic voxel space. The efficiency of the system in fast acquisition of high-quality 3D surface models is proven with different cultural heritage, household and industrial objects.