Real-Time Event-Based LED Tracking and Pose Estimation with PWM-Encoded Markers
Real-Time Event-Based LED Tracking and Pose Estimation with PWM-Encoded Markers
This paper introduces a real-time event-based framework for tracking LEDs encoded with pulse-width modulation (PWM) and estimating their six-degree-of-freedom relative pose. By using multiple LEDs sharing a common blinking frequency with distinct PWM duty cycles, the method reduces computational cost while maintaining robust identity association under rapid motion. The authors evaluate the approach on a self-collected dataset and demonstrate closed-loop drone guidance via experiments with a quadrotor and Mecanum-wheeled mobile robot under time-varying HDR illumination, with onboard deployment on an NVIDIA Jetson Orin NX. A dataset and implementation will be released to support further research.
The authors present a real-time event-based framework for tracking PWM-encoded LED markers and estimating 6-DoF pose, validated on a self-collected dataset and a quadrotor-Mecanum robot platform under HDR illumination; claims of robustness are supported by reported evaluations but deployment readiness remains specific to the tested hardware configuration.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Dataset details reported by the source
- Modalities
- event cameras Open source S2LEDs Open source S2
- Tasks
- LED tracking Open source S3pose estimation Open source S3
- code: not_reported
- weights: not_reported
- dataset: Reported pending Open source S5
- paper: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ordinated robotic platform comprising a quadrotor and a Mecanum-wheeled mobile robot, demonstrating the proposed framework's potential for closed-loop drone guidance through hovering and target-following experiments under time-varying HDR illumination, with onboard deployment on an NVIDIA Jetson Orin NX. The dataset and implementation will be released to facilitate further research.
Open source S5
arXiv:2611.07630v1 Announce Type: new Abstract: Event cameras are neuromorphic vision sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, supporting state estimation under rapid motion, motion blur, and high dynamic range (HDR) illumination. This also enables capture of high-frequency blinking patterns from light-emitting diodes (LEDs). Based on this, we
Open source S2
propose a real-time event-based framework for LED tracking and six-degree-of-freedom (6-DoF) relative pose estimation using pulse-width modulation (PWM)-encoded LED markers. Multiple LEDs share a common blinking frequency and encode identities through distinct PWM duty cycles, reducing frequency-resource requirements and computational cost. Our dual-rate asynchronous tracking framework combines low-rate LED detection
Open source S3
with high-rate raw-event updates, maintaining robust LED identity association and accurate pixel-level tracking under rapid motion. LED identities and positions establish reliable 2D-3D correspondences for real-time metric 6-DoF relative pose estimation. Extensive evaluations on a self-collected dataset demonstrate robustness under large motion flow and transient image-plane LED overlap. Furthermore, we develop a co
Open source S4
Real-Time Event-Based LED Tracking and Pose Estimation with PWM-Encoded Markers
Open source S1
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Source：arXiv Robotics — research abstracts · arxiv.org