Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry

TL;DR

Swarm-LIO introduces a decentralized LiDAR-inertial odometry system enabling precise, real-time state estimation for drone swarms, even in GPS-denied and challenging environments, by tightly integrating mutual observations with onboard sensors.

Contribution

This work presents a novel decentralized approach for swarm state estimation using LiDAR-inertial data, including a new drone detection and tracking method, achieving centimeter-level accuracy.

Findings

Achieves centimeter-level localization accuracy in real-world tests.

Demonstrates robustness in GPS-denied and challenging environments.

Outperforms existing LiDAR-inertial odometry methods for single UAVs.

Abstract

Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detection, identification and tracking method is proposed to obtain observations of teammate drones. The mutual observation measurements are then tightly-coupled with IMU and LiDAR measurements to perform real-time and accurate estimation of ego-state and relative state jointly. Extensive real-world experiments show the broad adaptability to complicated scenarios, including GPS-denied scenes, degenerate scenes for camera (dark night) or LiDAR (facing a single wall). Compared with ground-truth provided by motion capture system, the result shows the centimeter-level localization accuracy which outperforms other state-of-the-art LiDAR-inertial odometry for single UAV system.