同步相机轨迹重建新方法,无需精确时间戳也能高精度定位移动目标
3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras
- 基于异步相机的轨迹交点法,突破传统三角测量对时间同步的依赖
- 联合优化相机时序、姿态与目标运动参数,实现无精确时间信息下的三维轨迹重建
- 特别适合相机姿态不准或时间不同步场景,实测定位误差仅112.95米
光力学是固体力学的重要分支。点目标定位是光学实验力学中的基础问题,在无人机等多种任务中应用广泛。移动目标定位对分析其运动特征和动力学特性至关重要。从异步相机中重建点目标的三维轨迹是一项重大挑战,涉及轨迹重建与相机同步两个耦合子问题。现有方法通常只解决其中一个。本文提出一种基于异步相机的点目标三维轨迹重建方法,同时解决两个子问题。首先,将轨迹交点法扩展至异步相机,克服传统三角测量需同步相机的限制。其次,基于成像机理和目标动力学特性,建立相机时间信息与目标运动模型,并联合优化参数,实现无需精确时间参数的轨迹重建。第三,联合优化相机旋转、时间信息与目标运动参数,施加更紧致连续的运动约束,显著提升重建精度,尤其在相机旋转不准确时表现优异。仿真与真实实验结果表明该方法可行且高效。真实数据测试显示,在15~20公里观测距离下,定位误差达112.95米。
原文摘要 · Abstract (English)
Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchronization. Present methods typically address only one of these sub-problems individually. This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras, simultaneously solving both sub-problems. Firstly, we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization. Secondly, we develop models for camera temporal information and target motion, based on imaging mechanisms and target dynamics characteristics. The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters. Thirdly, we optimize the camera rotations alongside the camera time information and target motion parameters, using tighter and more continuous constraints on moving points. The reconstruction accuracy is significantly improved, especially when the camera rotations are inaccurate. Finally, the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method. The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation range of 15 ~ 20 km.
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