Relative State Estimation using Event-Based Propeller Sensing

2026-04-20

AI Summary

This work addresses the challenge of achieving low-latency, high-accuracy relative state estimation for multi-UAV systems in visually degraded environments. The authors propose a novel event camera–based approach that detects quadrotor propeller regions and temporally segments the event stream to estimate propeller rotation frequency, which is then used as a thrust input to drive kinematic state estimation. By fusing this frequency-derived motion cue with pose and camera position measurements recovered via elliptical fitting of propeller silhouettes, the method enables decentralized relative localization. Notably, it achieves the first real-world outdoor demonstration of event-based propeller frequency estimation with less than 3% error, overcoming the simulation-only limitation of prior works and offering a robust, low-latency solution for relative state estimation in multi-UAV systems.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond-level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing. The propellers in the event stream are tracked by detection to extract the region-of-interests. The event streams in these regions are processed in temporal chunks to estimate per-propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera-derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body-frame tilt-axis. The existing event-based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real-world outdoor flight sequences, providing a method for decentralized relative localization for multi-robot systems using event camera.

Problem

• relative state estimation
• event-based sensing
• multi-UAV systems
• propeller sensing
• autonomous swarms

Innovation

• event-based vision
• propeller sensing
• relative state estimation
• quadrotor localization
• decentralized multi-UAV