Relative State Estimation using Event-Based Propeller Sensing

TL;DR

This paper introduces an event-based propeller sensing framework for quadrotor relative state estimation, achieving low-error frequency estimation and enabling decentralized localization in multi-robot UAV systems.

Contribution

It presents a novel method for estimating propeller frequency and orientation using event cameras, improving speed and robustness over traditional monocular camera approaches.

Findings

Propeller frequency estimated with under 3% error in real-world tests.

Framework enables decentralized relative localization for multi-UAV systems.

Uses geometric primitives for orientation estimation from event streams.

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.