Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction

2026-09-15

AI Summary

该研究提出了一种基于因子图的概率框架，通过结合物理先验、传感器测量和时间平滑约束来估计可变形物体的网格状态，解决了机器人和仿真中可变形物体状态估计的问题。

Abstract

Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects. The method directly updates a tetrahedral mesh, a rich and physically-grounded representation of an environment, by combining physics priors, noisy sensor measurements, and temporal smoothness constraints within a unified probabilistic formulation. The estimation problem is posed as a nonlinear least-squares optimization and solved using Levenberg-Marquardt. Ex vivo central-airway obstruction experiments and simulations on deforming cube models demonstrate reliable and accurate reconstruction under both rigid motion and deformation, highlighting the potential of this probabilistic approach for principled, measurement-driven mesh state estimation in deformable object reconstruction.

Problem

• deformable object
• state estimation
• robotics
• simulation

Innovation

• factor graph
• probabilistic mesh state estimation
• tetrahedral mesh
• nonlinear least-squares optimization
• Levenberg-Marquardt