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

2026-08-10

AI Summary

This work addresses the challenge of pose estimation in space-constrained environments, where conventional multi-point PnP methods are difficult to deploy and fail to exploit available ego-motion priors such as known height and tilt angle. The authors propose a minimal pose solver requiring only two active LED markers, uniquely incorporating height and tilt constraints into a two-point geometric model. They derive both a closed-form solution and a linear least-squares formulation, and provide a systematic analysis of degenerate configurations. By fusing event camera data with IMU and altimeter measurements within the proposed geometric framework, the method significantly outperforms existing P2P approaches on both synthetic and real-world datasets, achieving accuracy comparable to P3P while demonstrating superior efficiency, accuracy, and robustness.

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.

Problem

• pose estimation
• event cameras
• active LED markers
• minimal solver
• height constraint

Innovation

• minimal solver
• event camera
• active LED markers
• pose estimation
• height constraint