超越控制点：基于不完整或缺失控制场的视觉测量平台的角秒级相对运动估计
Beyond Control Points: Arcsecond Relative-Motion Estimation of Vision Measurement Platforms With Incomplete or Absent Control Fields
- Shenzhen University（深圳大学）
- Shenzhen Expressway Co., Ltd.（深圳高速公路股份有限公司）
- Hunan University（湖南大学）
- Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究提出控制自适应差分框架，无需非线性优化或初始位姿，可实现视觉测量平台的角秒级相对运动估计，在多场景下达到最优精度与效率。
中文摘要 AI 辅助
基于视觉的远程变形监测对相机平台的运动高度敏感。绝对位姿差分通常依赖专用控制数据，并将两个独立的位姿误差传播到相对运动估计中。我们开发了一种控制自适应差分框架，可直接从图像位移和已知3D点估计帧间平台运动。在无专用控制点时，该框架可从测量点观测值中恢复平台旋转；一个经测量的控制点可实现先验约束的平移恢复，而两条不平行的控制射线可恢复完整的3D平移。该框架既不需要非线性优化，也不需要初始位姿估计。将控制数据排除在旋转阶段之外，可使旋转估计完全不受限于控制场的污染；其继承的差分公式也可完全抵消平移外参误差。我们推导了旋转可观测性条件、未建模平移和非刚性点运动的泄漏界，以及单点轴向先验偏差规律。在0.5像素图像噪声、最大30角分的姿态变化、最大2毫米的3D点扰动条件下，多相机估计器的旋转均方根误差（RMSE）为2.97角秒，平均运行时间为0.46毫秒；使用一个经测量的控制点时，其先验约束的平移RMSE为1.19毫米。在无稳定控制场的桥梁实验中，相对于全站仪测量的中位数坐标方向位移RMSE为0.85毫米。该估计器在测试的公共RGB-D和立体序列的3D坐标扰动下也保持零发散。这些结果在评估方法中实现了最先进的精度、校准鲁棒性和计算效率。
英文摘要
Long-range vision-based deformation monitoring is highly sensitive to motion of the camera platform. Absolute-pose differencing typically relies on dedicated control data and propagates two independent pose errors into the relative-motion estimate. We develop a control-adaptive differential framework that estimates inter-frame platform motion directly from image displacements and known 3D points. With no dedicated control point, the framework recovers platform rotation from measurement-point observations. One surveyed control point enables prior-constrained translation recovery, while two nonparallel control rays recover full 3D translation. The framework requires neither nonlinear optimization nor an initial pose estimate. Excluding control data from the rotation stage makes the rotation estimate exactly immune to contamination confined to the control field. The inherited differential formulation also cancels translational extrinsic errors exactly. We derive the rotation observability condition, a leakage bound for unmodeled translation and nonrigid point motion, and the single-point axial-prior bias law. Under 0.5-pixel image noise, attitude changes of up to 30~arcmin, and 3D point perturbations of up to 2~mm, the multi-camera estimator achieves a rotation RMSE of 2.97~arcsec and an average runtime of 0.46~ms. With one surveyed control point, its prior-constrained translation RMSE is 1.19~mm. In a bridge experiment without a stable control field, the median coordinate-wise displacement RMSE relative to total-station measurements is 0.85~mm. The estimator also maintains zero divergence under the tested 3D coordinate perturbations on public RGB-D and stereo sequences. These results establish state-of-the-art accuracy, calibration robustness, and computational efficiency among the evaluated methods.
