发表机构
Graz University of Technology; University of Picardie Jules Verne; University of Burgundy（格拉茨工业大学; 皮卡迪儒勒·凡尔纳大学; 勃艮第大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对空间受限场景的位姿估计难题，提出结合机载传感器高度与倾斜角的事件相机主动LED标记点两点最小求解器，实验显示其精度优于P2P、性能与P3P相当。
AI 中文摘要
在许多需要实时定位的自主应用中，基于主动标记点的系统相比计算密集型的基于特征的方法，具有低延迟、易部署的优势，因此更受青睐。事件相机具备高时间分辨率和极低延迟，常与主动LED标记点配合用于鲁棒的实时定位。现有方法通常依赖 Perspective-n-Point（PnP）求解器进行位姿估计，但结构化标记点布局在空间受限场景中部署难度较大，而机载传感器可轻易获取部分自运动信息（如重力方向和高度）。我们推导了一种鲁棒且准确的最小求解器，该求解器通过结合机载传感器（如IMU或高度计）测量的已知倾斜角和相机高度，仅从两个LED标记点估计相机位姿。所提公式通过闭式解和线性最小二乘解唯一确定相机位姿。我们进一步分析退化配置，明确高度信息对旋转估计无贡献的条件。为评估性能，我们开发了基于事件的主动标记点系统，结合动作捕捉系统的真值采集真实世界数据。对合成数据和真实数据的实验表明，所提方法相比当前最优P2P求解器精度更高，且性能与P3P相当。
英文摘要
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.
Comments8 pages, 6 figures, accepted by IEEE/RSJ International Conference on INTELLIGENT ROBOTS & SYSTEMS (IROS) 2026
