Online Next-Best-View Planning for Accuracy Optimization Using an Extended E-Criterion

摘要

Next-best-view (NBV) planning is an important aspect for three-dimensional (3D) reconstruction within controlled environments, such as a camera mounted on a robotic arm. NBV methods aim at a purposive 3D reconstruction sustaining predefined goals and limitations. Up to now, literature mainly presents NBV methods for range sensors, model-based approaches or algorithms that address the reconstruction of a finite set of primitives. For this work, we use an intensity camera without active illumination. We present a novel combined online approach comprising feature tracking, 3D reconstruction, and NBV planning that addresses arbitrary unknown objects. In particular we focus on accuracy optimization based on the reconstruction uncertainty. To this end we introduce an extension of the statistical E-criterion to model directional uncertainty, and we present a closed-form, optimal solution to this NBV planning problem. Our experimental evaluation demonstrates the effectivity of our approach using an absolute error measure.