A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionRobotics
AI summaryⓘ
The authors created a new method to figure out where a camera is pointing based on just two LED markers, using extra information like the tilt of the camera and how high it is from sensors onboard. This is useful because typical methods need more markers, which can be hard to place in tight spaces. Their approach can quickly and accurately find the camera's position in a way that can be solved with simple math. They tested their method with both simulated and real-world data and found it works better than some existing methods and just as well as others. They also studied when certain sensor data doesn't help in figuring out the camera’s rotation.
active marker systemevent camerapose estimationPerspective-n-Point (PnP)IMUaltimeterminimal solvercamera localizationdegenerate configurationleast-squares solution
Authors
Runze Yuan, Alexander Kappler, Jun Zhang, Kuangyi Chen, Fabio Morbidi, Pascal Vasseur, Cédric Demonceaux, Friedrich Fraundorfer
Abstract
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.