AWARE: Adaptive Whole-body Active Rotating Control for Enhanced LiDAR-Inertial Odometry under Human-in-the-Loop Interaction
Yizhe Zhang, Jianping Li, Liangliang Yin, Zhen Dong, Bisheng Yang

TL;DR
AWARE is a bio-inspired adaptive yaw control framework that enhances LiDAR-inertial odometry for UAVs in complex environments by exploiting rotational agility and reinforcement learning.
Contribution
It introduces a novel whole-body active yawing method using differentiable MPC and RL to improve state estimation without additional hardware.
Findings
AWARE significantly reduces odometry drift in feature-sparse scenes.
The framework improves navigation accuracy and stability in diverse environments.
Experimental results validate the effectiveness of adaptive yaw control in real-world UAV operations.
Abstract
Human-in-the-loop (HITL) UAV operation is essential in complex and safety-critical aerial surveying environments, where human operators provide navigation intent while onboard autonomy must maintain accurate and robust state estimation. A key challenge in this setting is that resource-constrained UAV platforms are often limited to narrow-field-of-view LiDAR sensors. In geometrically degenerate or feature-sparse scenes, limited sensing coverage often weakens LiDAR Inertial Odometry (LIO)'s observability, causing drift accumulation, degraded geometric accuracy, and unstable state estimation, which directly compromise safe and effective HITL operation and the reliability of downstream surveying products. To overcome this limitation, we present AWARE, a bio-inspired whole-body active yawing framework that exploits the UAV's own rotational agility to extend the effective sensor horizon and…
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