用物理模型让相机在会变形的机器人上也能准确定位。
Metric, inertially aligned monocular state estimation via kinetodynamic priors
- 用神经网络学弹性形变，用B样条建模连续运动
- 在单目视觉下同时恢复尺度和重力方向
- 适合动态变形机器人，如柔性臂、可伸缩平台
柔性机器人系统的状态估计面临严峻挑战，尤其当结构动态变形时，传统刚体假设失效。本文提出一种融合动力学先验的单目状态估计算法，将现有刚体方法拓展至非刚性系统。核心包括：首先，利用多层感知机高效学习基于形变-受力的弹性模型；其次，采用连续时间B样条运动模型捕捉平台平滑运动。通过持续应用牛顿第二定律，建立视觉轨迹加速度与形变诱导加速度之间的关系。实验表明，该方法不仅实现非刚性平台的鲁棒高精度定位，还能通过合理建模平台物理特性恢复惯性传感信息。在弹簧-相机系统上验证了可行性，成功解决了单目视觉里程计中通常病态的尺度与重力恢复问题。
原文摘要 · Abstract (English)
Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structures that invalidate rigid-body assumptions. This paper addresses this problem and enables the extension of existing rigid-body pose estimation methods to non-rigid systems. Our approach integrates two core components: first, we capture elastic properties using a deformation-force model, efficiently learned via a Multi-Layer Perceptron; second, we resolve the platform's inherently smooth motion using continuous-time B-spline kinematic models. By continuously applying Newton's Second Law, our method formulates the relationship between visually-derived trajectory acceleration and predicted deformation-induced acceleration. We demonstrate that our approach not only enables robust and accurate pose estimation on non-rigid platforms, but also shows that the properly modeled platform physics allow for the recovery of inertial sensing properties. We validate this feasibility on a simple spring-camera system, showing how it robustly resolves the typically ill-posed problem of metric scale and gravity recovery in monocular visual odometry.
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