基于运动动力学先验的度量惯性对齐单目状态估计
Metric, inertially aligned monocular state estimation via kinetodynamic priors
AI总结：
本文提出基于运动动力学先验的单目状态估计方法，通过弹性质建模和连续时间B样条运动学模型，实现非刚性系统的鲁棒准确姿态估计，并恢复惯性传感特性。
摘要：
灵活机器人系统的精确状态估计面临重大挑战，特别是具有动态变形结构的平台破坏了刚体假设。本文解决此问题，扩展现有刚体姿态估计方法到非刚性系统。我们的方法整合两个核心组件：首先，使用变形力模型捕捉弹性质，通过多层感知机高效学习；其次，利用连续时间B样条运动学模型解决平台的平滑运动。通过持续应用牛顿第二定律，我们的方法建立了视觉推导轨迹加速度与预测变形引起的加速度之间的关系。我们证明了该方法不仅在非刚性平台上实现了鲁棒和准确的姿态估计，而且正确建模的平台物理允许恢复惯性传感特性。我们在简单的弹簧-相机系统上验证了这一可行性，展示了如何在单目视觉里程计中稳健地解决通常病态的度量尺度和重力恢复问题。
英文摘要：
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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