Deformable structure from motion by fusing visual and inertial measurement data

S. Giannarou, Zhiqiang Zhang, Guang-Zhong Yang
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引用次数: 14

Abstract

Accurate recovery of the 3D structure of a deforming surgical environment during minimally invasive surgery is important for intra-operative guidance. One key component of reliable reconstruction is accurate camera pose estimation, which is challenging for monocular cameras due to the paucity of reliable salient features, coupled with narrow baseline during surgical navigation. With recent advances in miniaturized MEMS sensors, the combination of inertial and vision sensing can provide increased robustness for camera pose estimation particularly for scenes involving tissue deformation. The aim of this work is to propose a robust framework for intra-operative free-form deformation recovery based on structure-from-motion. A novel adaptive Unscented Kalman Filter (UKF) parameterization scheme is proposed to fuse vision information with data from an Inertial Measurement Unit (IMU). The method is built on a compact scene representation scheme suitable for both surgical episode identification and instrument-tissue motion modelling. Detailed validation with both synthetic and phantom data is performed and results derived justify the potential clinical value of the technique.
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通过融合视觉和惯性测量数据,使结构从运动中变形
在微创手术中,准确恢复变形手术环境的三维结构对术中指导非常重要。可靠重建的一个关键组成部分是准确的相机姿态估计,由于缺乏可靠的显著特征,加上手术导航时基线狭窄,这对单目相机来说是一个挑战。随着微型化MEMS传感器的最新进展,惯性和视觉传感的结合可以为相机姿态估计提供更高的鲁棒性,特别是对于涉及组织变形的场景。这项工作的目的是提出一个基于运动结构的术中自由变形恢复的鲁棒框架。提出了一种新的自适应无气味卡尔曼滤波(UKF)参数化方案,将视觉信息与惯性测量单元(IMU)数据融合在一起。该方法是建立在一个紧凑的场景表示方案,适用于手术事件识别和器械组织运动建模。对合成和假体数据进行了详细的验证,得出的结果证明了该技术的潜在临床价值。
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