高さ制約付き2点最小解法：イベントカメラとアクティブLEDマーカーによる姿勢推定
A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras
IMUや高度計から得られる傾き角とカメラ高さを利用し、2つのLEDマーカーのみでカメラ姿勢を推定する最小解法を提案した。閉形式と線形最小二乗の両方で解を導出し、実データで精度を検証した。
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著者: Runze Yuan, Alexander Kappler, Jun Zhang, Kuangyi Chen, Fabio Morbidi, Pascal Vasseur, Cédric Demonceaux, Friedrich Fraundorfer
分類: cs.CV, cs.RO
原文アブストラクト
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.
