  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
resource-constrained 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.
