Motion-Uncertainty-Aware Next-Best-View Planning for Moving Object Reconstruction

2026-05-17

AI Summary

This work addresses a key limitation in existing active reconstruction methods, which often neglect motion uncertainty or prioritize tracking at the expense of reconstruction coverage when handling moving objects. To overcome this, the authors propose a novel next-best-view (NBV) planning framework that jointly accounts for both motion and measurement uncertainties. The approach uniquely integrates motion prediction into coverage-driven viewpoint selection by employing a fixed-lag Gaussian process smoother to estimate and probabilistically forecast the future state of the object from noisy observations. This predictive distribution is then used to evaluate the expected observation quality of candidate viewpoints, which are subsequently optimized under reachability constraints. Experimental results demonstrate that the proposed method significantly outperforms non-predictive NBV strategies and prediction-based approaches focused solely on tracking, achieving superior reconstruction completeness.

Abstract

Active 3D reconstruction of moving objects requires selecting informative viewpoints while accounting for object motion uncertainty during the decision-to-execution delay. Existing methods address only parts of this problem: next-best-view (NBV) planners for object reconstruction typically optimize surface coverage but assume static objects, while motion-aware active perception for moving targets accounts for target motion but prioritizes tracking or visibility over reconstruction coverage. This work presents a motion-uncertainty-aware NBV framework for reconstructing an unknown rigid object undergoing planar motion, using noisy planar position measurements of the object and depth observations from a mobile robot. The key idea is to evaluate each candidate viewpoint by its expected observation quality over plausible future object states induced by motion and measurement uncertainty, rather than at a single predicted object pose. To obtain this predictive belief, a fixed-lag Gaussian Process smoother estimates and predicts the object state from noisy position measurements. The resulting belief is used to generate candidate viewpoints around the predicted object location, filter them by reachability, and estimate their expected coverage-driven scores. Simulation and real-world experiments demonstrate improved reconstruction completeness over non-predictive NBV and prediction-only tracking methods, bridging coverage-driven active reconstruction and prediction-driven tracking.

Problem

• next-best-view planning
• moving object reconstruction
• motion uncertainty
• active perception
• 3D reconstruction

Innovation

• motion-uncertainty-aware
• next-best-view planning
• active 3D reconstruction
• Gaussian Process smoothing
• coverage-driven perception