arXiv:2608.09520cs.CVcs.RO2026-08中稿 · IEEE/RSJ Internati…

仅用两个LED标记+高度信息,实现高精度实时位姿估计

A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras

论文配图:A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras
图 1 · 摘自论文原文
  • 利用已知俯仰角和高度信息,通过闭式解与最小二乘法求解双标记位姿
  • 在真实数据上误差比现有最先进方法低23%,接近三标记解的精度
  • 适合空间受限场景,尤其适用于带IMU/气压计的无人机等移动设备

在需要实时定位的自主应用中,主动标记系统因延迟低、部署简便而优于计算量大的基于特征的方法。事件相机具备高时间分辨率和极低延迟,常与主动LED标记结合用于鲁棒实时定位。现有方法通常依赖透视n点(PnP)求解器,但结构化标记布局在空间受限场景下难以部署,而部分自运动信息(如重力方向和海拔)可由机载传感器轻松获取。本文推导出一种鲁棒且精确的最小化求解器,仅需两个LED标记,结合机载传感器测量的已知俯仰角与相机高度,即可估计相机位姿。该公式可通过闭式解和线性最小二乘法唯一确定位姿。我们进一步分析退化情形,明确了高度信息对旋转估计无贡献的条件。为评估性能,我们搭建了基于事件相机的主动标记系统,采集真实世界数据并使用动作捕捉系统提供真值。在合成与真实数据上的实验表明,该方法在精度上优于当前最先进的两点(P2P)求解器,且性能与三点(P3P)求解器相当。

原文摘要 · Abstract (English)

In many autonomous applications requiring real-time localization, active marker-based systems are preferred due to their low latency and ease of deployment compared to computationally demanding feature-based methods. Event~\mbox{cameras} offer high temporal resolution and minimal delay and are commonly used with active LED markers for robust real-time localization. Existing methods typically rely on Perspective-n-Point (PnP) solvers for pose estimation. However, structured marker layouts can be challenging to deploy in space-constrained scenarios, while partial self-motion information (e.g., gravity direction and altitude) is readily available from onboard sensors. We derive a robust and accurate minimal solver that estimates camera pose from only two LED markers by incorporating known tilt angle and camera height measured by an onboard sensor, such as an IMU or an altimeter. The proposed formulation uniquely determines the camera pose through both a closed-form and a linear least-squares solution. We further analyze degenerate configurations and characterize the conditions under which height information does not contribute to rotation estimation. For evaluation, we developed an event-based active marker system to collect real-world data with ground truth from a motion capture system. Experiments on both synthetic and real data demonstrate improved accuracy over the state-of-the-art P2P solver and competitive performance relative to P3P.

位姿估计事件相机主动标记无人机

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