Explore From Sketch: Accelerating UAV Exploration in Large-scale Environments with Prior Maps

2026-06-10

AI Summary

This work addresses the inefficiency of autonomous drone exploration in large-scale complex environments caused by suboptimal scheduling and excessive detours. To this end, we propose an efficient exploration method that leverages sparse, misaligned, and even biased 2D prior maps. Robust localization is achieved through a multi-hypothesis 2D–3D point cloud registration mechanism integrating GeoContext descriptors, multi-frame verification, and Scale-ICP. Furthermore, we introduce a risk-aware hierarchical viewpoint planning framework that jointly considers localization uncertainty and confidence-weighted travel risk, solving a fixed-endpoint Traveling Salesman Problem via Monte Carlo Tree Search. Experiments demonstrate that our approach improves exploration efficiency by up to 34.2% and reduces flight distance by 37.9% compared to state-of-the-art methods, while exhibiting strong robustness to missing or deformed prior maps in both simulated and real-world scenarios.

Abstract

Autonomous exploration with UAVs in large-scale, topologically complex environments often suffers from low efficiency due to suboptimal scheduling and detours. Prior maps (e.g., construction drawings), although usually imprecise and flawed, are readily available in many scenarios and have the potential to provide global structural guidance. This paper presents a novel exploration framework that leverages sparse, unaligned, and even discrepant 2D prior maps for LiDAR-based UAV exploration. First, a robust 2D-3D point cloud registration pipeline is proposed to align LiDAR observations with prior maps. The registration pipeline combines a GeoContext descriptor for single-frame candidate retrieval, a multi-frame verification mechanism for coarse transformation estimation with outlier rejection, and a Scale-ICP algorithm for refinement. The registration module can handle map discrepancies and provide multiple hypotheses when geometric ambiguities arise. To effectively utilize the registration results for exploration planning, we further develop a hierarchical viewpoint planning strategy under localization uncertainties. The hierarchical strategy first spatially attaches local viewpoints to prior guidepoints and adopts a Monte Carlo Tree Search solver to determine their traversal sequence under each registration hypothesis. To mitigate registration uncertainty, a risk-aware selector evaluates prior sequences using confidence-weighted travel risk, and a fixed-endpoint traveling salesman problem is formulated to generate an efficient local coverage path under the selected prior guidance. Benchmark evaluations reveal up to 34.2% improvement in exploration efficiency and 37.9% reduction in flight distance compared to state-of-the-art methods, while extensive simulations and field experiments further demonstrate robustness to prior map incompleteness and deformations.

Problem

• UAV exploration
• prior maps
• large-scale environments
• exploration efficiency
• LiDAR-based navigation

Innovation

• 2D-3D registration
• prior map guidance
• hierarchical viewpoint planning
• risk-aware exploration
• LiDAR-based UAV navigation