TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields.Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs.We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs.However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors.This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resourceconstrained BMAVs.We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically.TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms.We implement TransformLoc on industrial drones and validate its performance.Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.