发表机构
Shenzhen University; Sun Yat-sen University（深圳大学; 中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出差分6-DOF位姿估计方法，避免独立绝对位姿估计，可证抗一阶相机外参误差，在微运动估计上优于PnP等方法，单目、双目求解器精度与效率优异。
AI 中文摘要
精确的6自由度（6-DOF）运动估计对机器人操作、自主系统和结构位移监测至关重要。传统3D-2D方法在每个时刻独立估计绝对相机位姿，并通过相机-平台外参恢复平台运动，使其对外参标定误差敏感，尤其在微运动场景下。本文提出一种差分位姿估计方法，可直接从帧间图像位移和已知3D控制点恢复平台运动。该方法通过对透视投影方程求差、采用深度不变性近似并在特殊欧几里得群SE(3)上建模运动，避免了独立的绝对位姿估计，支持单目和多相机系统。我们证明平移外参误差会完全抵消，而旋转误差会产生由标定误差、运动幅度和观测几何决定的有界扰动。此外，我们推导了通用可观测性条件、克拉美-罗下界和无偏一致估计器，并表征了近似的有效性极限。大量合成与真实实验建立了6-DOF平台微运动估计的新state-of-the-art，在精度、标定鲁棒性和计算效率上优于代表性的PnP和generalized-PnP方法。在5个控制点和0.5像素图像噪声下，单目求解器的俯仰-偏航旋转均方根误差（RMSE）为10.09角秒，平移RMSE为3.70毫米，运行时间为0.34毫秒；双目求解器的旋转RMSE为10.58角秒，平移RMSE为3.91毫米，运行时间为0.27毫秒。代码将在发表后发布于该https URL。
英文摘要
Accurate six-degree-of-freedom (6-DOF) motion estimation is essential for robotic manipulation, autonomous systems, and structural displacement monitoring. Conventional 3D-2D methods estimate absolute camera poses independently at each time and recover platform motion through camera-to-platform extrinsics, making them sensitive to extrinsic calibration errors, especially for micromotion. We present a differential pose estimation method that directly recovers platform motion from inter-frame image displacements and known 3D control points. By differencing perspective projection equations, using a depth-invariance approximation, and modeling motion on SE(3), the method avoids independent absolute-pose estimation and supports both monocular and multi-camera systems. We prove that translational extrinsic errors cancel exactly, while rotational errors induce a bounded perturbation determined by calibration error, motion magnitude, and observation geometry. We also derive generic observability conditions, a Cramer-Rao lower bound, and a bias-eliminated consistent estimator, and characterize the validity limits of the approximations. Extensive synthetic and real-world experiments establish a new state of the art for 6-DOF platform micromotion estimation, outperforming representative PnP and generalized-PnP methods in accuracy, calibration robustness, and computational efficiency. With five control points and 0.5-pixel image noise, the monocular solver obtains a combined pitch-yaw rotation RMSE of 10.09 arcsec, a translation RMSE of 3.70 mm, and a runtime of 0.34 ms. The binocular solver achieves a rotation RMSE of 10.58 arcsec, a translation RMSE of 3.91 mm, and a runtime of 0.27 ms. Code will be released upon publication at https://github.com/zyoungszu/pami2026.
Comments16 pages, 15 figures
