DefVINS：用于变形场景的视觉-惯性里程计
DefVINS: Visual-Inertial Odometry for Deformable Scenes
AI总结：
本文提出DefVINS，一种针对变形场景的视觉-惯性里程计，通过分解里程状态为刚性IMU锚定部分和非刚性场景变形图，解决传统VIO对变形场景的不足。同时引入VIMandala基准测试集，并通过实验验证其优于传统方法。
摘要：
变形场景违反了经典视觉-惯性里程计（VIO）所依赖的刚性假设，常导致过度拟合局部非刚性运动或严重相机姿态漂移。本文介绍DefVINS，首个针对变形环境设计的视觉-惯性里程计管道。我们的方法通过将里程状态分解为刚性、IMU锚定部分和由嵌入变形图表示的非刚性场景变形来建模。作为第二项贡献，我们提出了VIMandala，首个包含真实图像和地面真实相机姿态的视觉-惯性里程计变形场景基准。此外，我们还通过合成Drunkard's基准集添加模拟惯性测量，以在受控条件下进一步评估我们的管道。我们还提供了视觉-惯性变形里程计问题的可观测性分析，阐明了惯性测量如何约束相机运动，并使在变形存在时原本不可观测的模式变得可识别。这种分析促使使用IMU锚定，并导致一种基于条件的激活策略，以避免在激励不足时出现不合理的更新。在合成Drunkard's和我们现实的VIMandala基准上的实验结果表明，DefVINS优于刚性视觉-惯性和非刚性视觉里程计基线。我们的源代码和数据将在接受后发布。
英文摘要：
Deformable scenes violate the rigidity assumptions underpinning classical visual--inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or to severe camera pose drift when deformation dominates visual parallax. In this paper, we introduce DefVINS, the first visual-inertial odometry pipeline designed to operate in deformable environments. Our approach models the odometry state by decomposing it into a rigid, IMU-anchored component and a non-rigid scene warp represented by an embedded deformation graph. As a second contribution, we present VIMandala, the first benchmark containing real images and ground-truth camera poses for visual-inertial odometry in deformable scenes. In addition, we augment the synthetic Drunkard's benchmark with simulated inertial measurements to further evaluate our pipeline under controlled conditions. We also provide an observability analysis of the visual-inertial deformable odometry problem, characterizing how inertial measurements constrain camera motion and render otherwise unobservable modes identifiable in the presence of deformation. This analysis motivates the use of IMU anchoring and leads to a conditioning-based activation strategy that avoids ill-posed updates under poor excitation. Experimental results on both the synthetic Drunkard's and our real VIMandala benchmarks show that DefVINS outperforms rigid visual--inertial and non-rigid visual odometry baselines. Our source code and data will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
