Real-Time Event-Based LED Tracking and Pose Estimation with PWM-Encoded Markers
Overview
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.
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Event evidence and corrections
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Artifact availability · dataset: reported pending
“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.”
Exact source · revision 1Source owner not reported
Dataset · modalities: event cameras · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Dataset · modalities: LEDs · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Dataset · tasks: LED tracking · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Dataset · tasks: pose estimation · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-11-09T20:24:52.144Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalReal-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.
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