一种在线自校准折射相机模型及其在水下里程计中的应用
An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry
AI总结：
本文提出一种适用于水下视觉惯性里程计的在线自校准折射相机模型，利用对极约束实时估计折射率并校正畸变，无需标定目标，实验验证了其对折射率估计的准确性及在跨介质条件下的鲁棒里程计性能。
摘要：
本工作提出了一种适用于水等折射介质的相机模型，并应用于水下视觉惯性里程计。该模型支持实时自校准，无需已知对应关系或标定目标。它可分离为畸变模型（取决于折射率 $n$ 和径向像素坐标）与虚拟针孔模型（作为 $n$ 的函数）。我们利用对极约束推导了自校准公式，以估计折射率并随后校正畸变。通过使用集成相机和惯性传感的水下机器人进行实验研究，验证了该模型对折射率的准确估计及其在扩展条件范围内对鲁棒里程计估计的益处。最后，我们展示了介质间的过渡以及在线估计变化的折射率，从而允许跨折射介质的计算机视觉任务。
英文摘要：
This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index $n$ and radial pixel coordinate) and a virtual pinhole model (as a function of $n$). We derive the self-calibration formulation leveraging epipolar constraints to estimate the refractive index and subsequently correct for distortion. Through experimental studies using an underwater robot integrating cameras and inertial sensing, the model is validated regarding the accurate estimation of the refractive index and its benefits for robust odometry estimation in an extended envelope of conditions. Lastly, we show the transition between media and the estimation of the varying refractive index online, thus allowing computer vision tasks across refractive media.
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。
