Visibility-aware Cooperative Aerial Tracking with Decentralized LiDAR-based Swarms

Abstract--Autonomous aerial tracking with drones offers vast potential for surveillance, cinematography, and industrial inspection applications. While single-drone tracking systems have been extensively studied, swarm-based target tracking remains underexplored, despite its unique advantages of distributed perception, fault-tolerant redundancy, and multidirectional target coverage. T o bridge this gap, we propose a novel decentralized LiDAR-based swarm tracking framework that enables visibility-aware, cooperative target tracking in complex environments, while fully harnessing the unique capabilities of swarm systems. T o address visibility, we introduce a novel Spherical Signed Distance Field (SSDF)-based metric for 3-D environmental occlusion representation, coupled with an efficient algorithm that enables real-time onboard SSDF updating. A general Field-of-View (FOV) alignment cost supporting heterogeneous LiDAR configurations is proposed for consistent target observation. These innovations are integrated into a hierarchical planner, combining a kinodynamic front-end searcher with a spatiotemporal SE(3) back-end optimizer to generate collision-free, visibility-optimized trajectories. The proposed approach undergoes thorough evaluation through comprehensive benchmark comparisons and ablation studies. Deployed on heterogeneous LiDAR swarms, our fully decentralized implementation features collaborative perception, distributed planning, and dynamic swarm reconfigurability. V alidated through rigorous real-world experiments in cluttered outdoor environments, the proposed system demonstrates robust cooperative tracking of agile targets (drones, humans) while achieving superior visibility maintenance. This work establishes a systematic solution for swarm-based target tracking, and its source code will be released to benefit the community. Recent studies highlight the unique suitability of UA Vs for tracking dynamic targets in complex environments, owing to their highly agile three-dimensional (3-D) maneuverability. While substantial progress has been made in single-UA V tracking, the swarm-based aerial tracking remains underexplored. The authors are with the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong. X. Lyu is with the School of Intelligent System Engineering, Sun Y at-sen University, Shenzhen, China. A swarm of four autonomous drones is cooperatively tracking a human runner using heterogeneous LiDAR configurations. The LiDAR setup consists of one upward-facing Mid360 LiDAR (marked by blue dashed lines), one downward-facing Mid360 LiDAR (green dashed lines), and two Avia LiDARs (red dashed lines). The swarm forms a 3-D distribution to track the target, with each tracker positioned optimally to suit its FOV settings. Effective agile aerial tracking with autonomous swarms primarily relies on three criteria: visibility, coordination, and portability.