PB-NBV: Efficient Projection-Based Next-Best-View Planning Framework for Reconstruction of Unknown Objects

TL;DR

This paper introduces PB-NBV, an efficient projection-based framework for next-best-view planning that reduces computational costs and improves 3D object reconstruction quality in robotic applications.

Contribution

The framework refits voxel clusters into ellipsoids and uses a projection-based evaluation, significantly reducing ray-casting and computational costs in NBV planning.

Findings

Achieves higher point cloud coverage than existing methods

Reduces computational time significantly

Validated in both simulation and real-world experiments

Abstract

Completely capturing the three-dimensional (3D) data of an object is essential in industrial and robotic applications. The task of next-best-view (NBV) planning is to calculate the next optimal viewpoint based on the current data, gradually achieving a complete 3D reconstruction of the object. However, many existing NBV planning algorithms incur heavy computational costs due to the extensive use of ray-casting. Specifically, this framework refits different types of voxel clusters into ellipsoids based on the voxel structure. Then, the next optimal viewpoint is selected from the candidate views using a projection-based viewpoint quality evaluation function in conjunction with a global partitioning strategy. This process replaces extensive ray-casting, significantly improving the computational efficiency. Comparison experiments in the simulation environment show that our framework achieves the highest point cloud coverage with low computational time compared to other frameworks. The real-world experiments also confirm the efficiency and feasibility of the framework. Our method will be made open source to benefit the community.