FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR

2026-05-14

AI Summary

This work addresses the limitations of conventional fixed-LiDAR drones in unknown environments, where rigid sensor mounting restricts perceptual coverage and necessitates inefficient maneuvers that degrade localization accuracy. To overcome this, the authors propose a drone platform equipped with an independently rotating LiDAR and a hierarchical exploration framework. The global planner organizes and prioritizes frontier regions based on topological structure, while the local planner employs a fused uncertainty-aware model predictive controller (FU-MPC) that explicitly treats the LiDAR’s yaw angle as a decision variable along predicted trajectories, jointly optimizing exploration gain and orientation-dependent localization uncertainty. This approach is the first to integrate scanning degrees of freedom into a unified exploration-localization optimization and enables real-time online computation through lightweight surrogate evaluation. Experiments demonstrate significantly improved exploration efficiency in complex environments while maintaining superior localization robustness compared to fixed-scanning and uncertainty-only baselines.

Abstract

Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe navigation in complex scenes depends on consistent mapping and pose estimation. However, for conventional LiDAR-equipped UAVs, the observable region is tightly coupled with the UAV pose and motion. Expanding coverage often requires additional translational or rotational maneuvers, which can reduce exploration efficiency and increase the risk of localization degradation in geometrically challenging environments. Motorized rotating LiDARs provide a promising solution by actively adjusting the sensor viewing direction without changing the UAV motion, thereby introducing an additional sensing degree of freedom. Nevertheless, existing exploration systems rarely exploit this scanning freedom as an explicit decision variable linked to both exploration progress and localization quality. To address this gap, we develop a UAV platform equipped with an independently actuated rotating LiDAR and propose a hierarchical exploration framework. The global planner organizes frontiers into representative viewpoints and sequences them using topology-aware transition costs. Built upon this planner, FU-MPC serves as a local receding-horizon scan controller that optimizes LiDAR rotation along the predicted flight trajectory. The controller jointly considers frontier-aware exploration utility and direction-dependent localization uncertainty, while lightweight surrogate evaluation enables real-time onboard execution. Experiments in complex environments demonstrate that the proposed system improves exploration efficiency while maintaining robust localization performance compared with fixed-pattern scanning and uncertainty-only baselines. The project page can be found at https://kafeiyin00.github.io/FU-MPC/.

Problem

• UAV exploration
• localization accuracy
• motorized LiDAR
• coverage efficiency
• sensor planning

Innovation

• Model Predictive Control
• Motorized LiDAR
• Frontier-aware Exploration
• Localization Uncertainty
• UAV Autonomy