A Global Max-Flow-Based Multi-Resolution Next-Best-View Method for Reconstruction of 3D Unknown Objects

Abstract

Many robot tasks, such as grasping and inspection, may require complete 3D models with enough surface details. Fully autonomous environment exploration and 3D reconstruction of unknown objects are challenging for a robot when little or no knowledge about an object is known as a priori. Previous work updated sensor measurements from a greedy ordered set of views into a map with an unchangeable voxel size, leading to the lack of details on the object surface, sampling problems, and no adaption to small objects. We propose a global max-flow-based multi-resolution next-best-view (NBV) method to improve performance on these problems. In particular, it utilizes a max-flow-based global view quality function to obtain optimal NBVs, and a multi-resolution strategy to optimize the reconstruction quality and efficiency. Results of comparative simulation experiments with state-of-the-art (SOTA) methods show that our method achieves a higher voxel coverage under the same resolution. Parametric and ablation studies confirm that the global view function and multi-resolution strategy are effective. Results of real-world experiments show that our method achieves complete reconstruction of small objects with only 6–8 views.