Deformable scenes violate the rigidity assumptions underpinning classical visual-inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or severe drift when deformation dominates visual parallax. We introduce DefVINS, a visual-inertial odometry framework that explicitly separates a rigid, IMU-anchored state from a non--rigid warp represented by an embedded deformation graph. The system is initialized using a standard VIO procedure that fixes gravity, velocity, and IMU biases, after which non-rigid degrees of freedom are activated progressively as the estimation becomes well conditioned. An observability analysis is included to characterize how inertial measurements constrain the rigid motion and render otherwise unobservable modes identifiable in the presence of deformation. This analysis motivates the use of IMU anchoring and informs a conditioning-based activation strategy that prevents ill-posed updates under poor excitation. Ablation studies demonstrate the benefits of combining inertial constraints with observability-aware deformation activation, resulting in improved robustness under non-rigid environments.

翻译：可变形场景违背了经典视觉-惯性里程计所依赖的刚性假设，这通常导致算法过度拟合局部的非刚性运动，或在形变主导视觉视差时产生严重的漂移。我们提出了DefVINS，一种视觉-惯性里程计框架，它显式地将一个由IMU锚定的刚性状态与一个由嵌入式变形图表示的非刚性形变分离开来。该系统通过一个标准的VIO流程进行初始化，该流程固定了重力、速度和IMU偏差，随后非刚性自由度在估计条件良好时被逐步激活。本文包含了一项可观测性分析，以描述惯性测量如何约束刚性运动，并使在形变存在情况下原本不可观测的模式变得可识别。该分析为使用IMU锚定提供了依据，并启发了一种基于条件数的激活策略，以防止在激励不足时出现病态更新。消融实验证明了将惯性约束与可观测性感知的形变激活相结合的优势，从而在非刚性环境下实现了更高的鲁棒性。