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

TL;DR

This paper introduces a novel next-best-view planning framework that accounts for object motion uncertainty to improve 3D reconstruction of moving objects using a mobile robot.

Contribution

It presents a motion-uncertainty-aware NBV method that predicts future object states and evaluates viewpoints based on expected observation quality, enhancing reconstruction completeness.

Findings

Improved reconstruction completeness over non-predictive methods

Effective integration of motion prediction and coverage optimization

Validated through simulation and real-world experiments

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.