Visibility-Aware Cooperative Tracking With Decentralized LiDAR-Based Aerial Swarms

Abstract

Autonomous aerial tracking with drones offers vast potential for surveillance, cinematography, and industrial inspection. While single-drone tracking has been extensively studied, swarm-based target tracking remains underexplored despite its advantages in distributed perception, fault tolerance, and multidirectional coverage. In this article, we propose a decentralized a decentralized swarm tracking framework based on Light Detection and Ranging (LiDAR) sensors that enablesvisibility-aware cooperative tracking in complex environments. To address visibility, we introduce a novel spherical signed distance field (SSDF)-based metric for 3-D environmental occlusion representation, coupled with an algorithm that enables real-time onboard SSDF updating. A general field-of-view alignment cost supporting heterogeneous LiDAR configurations is proposed for consistent target observation. Swarm coordination is enhanced through costs that enforce inter-robot clearance, prevent mutual occlusions, and facilitate 3-D multidirectional target encirclement via a novel electrostatic-potential-inspired distribution metric. These are integrated into a hierarchical planner combining a kinodynamic front-end searcher with a spatiotemporal $\text{SE}(3)$ back-end optimizer. The proposed approach is thoroughly evaluated through comprehensive benchmark comparisons and ablation studies. Deployed on heterogeneous LiDAR swarms, the fully decentralized system supports collaborative perception, distributed planning, and dynamic reconfigurability. Validated through extensive real-world experiments in cluttered outdoor environments, the proposed system demonstrates robust cooperative tracking of dynamic targets (drones, humans) while achieving visibility maintenance.