发表机构
University of Connecticut; DSO National Laboratories（康涅狄格大学; 新加坡国防科技局实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对利用无人机异步离散GPS轨迹标定固定相机的问题，建立参数估计模型，提出基于迭代最小二乘的最大似然估计器，推荐合适无人机轨迹，仿真验证其估计性能达到CRLB。
AI 中文摘要
本文研究固定相机的标定问题，该问题利用GPS记录的无人机轨迹来估计相机的偏航角、俯仰角和滚转角。将GPS轨迹作为相机标定的地面真值存在三个挑战：第一，GPS数据的高度存在未知偏差，不准确；第二，GPS接收器与相机未实现时间同步，两者系统间存在未知时间偏移；第三，GPS轨迹是时间离散的，需要进行精确插值，而由于还需用到速度信息，这本质上是一个估计问题。为解决前两个挑战，本文将该问题建模为参数估计问题，以估计包含GPS高度偏差、时间偏移以及相机偏航、俯仰、滚转角偏差的向量。针对第三个挑战，本文开发了一种特殊的最大似然估计器，该估计器采用迭代最小二乘算法，可处理非同步的离散时间GPS轨迹。由于相机测量误差通常较小，标定需要高精度，使得标定后的残余偏差误差与测量误差标准差相比不应显著，而标定精度高度依赖于无人机轨迹。本文还推荐了一种可实现良好标定精度的合适无人机轨迹，其标定精度可达测量误差标准差的14%。通过仿真测试验证了算法性能，估计结果达到了克拉美罗下界（CRLB），因为相对于CRLB的归一化估计误差平方在统计上是可接受的。
英文摘要
This paper considers a stationary camera calibration problem, which estimates the camera orientation angles yaw, pitch and roll, using a drone trajectory recorded by a GPS. There are three challenges in using a GPS trajectory as ground truth for camera calibration. One, the altitude of GPS data is inaccurate with an unknown bias. Two, the GPS receiver and camera are not time synchronized, and there is an unknown time offset between the two systems. Three, the GPS trajectory is time-discrete and accurate interpolation is needed. This is actually an estimation problem since velocity is also needed. To address the first two challenges, we formulate the problem as a parameter estimation problem to estimate a vector consisting of the GPS altitude bias and time offset in addition to the camera yaw, pitch and roll biases. We then develop a special maximum likelihood estimator using the Iterated Least Squares algorithm which can work with a non-synchronized time-discrete GPS trajectory for the third challenge. Since the camera measurement errors are usually small, this requires a high calibration accuracy so that the residual bias error following the calibration should not be significant compared to the measurement error standard deviation. The calibration accuracy depends highly on the drone trajectory. This paper also recommends an appropriate drone trajectory which can yield a good calibration accuracy, namely, 14\% of the measurement error standard deviation. Simulation tests are conducted to demonstrate the algorithm performance. The estimation results meet the Cramer-Rao Lower Bound (CRLB) since the Normalized Estimation Error Squared w.r.t.\ the CRLB is statistically acceptable.
Comments11 pages, 12 figs, published on JAIF
