REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs

TL;DR

This paper introduces a novel UAV exploration method combining pre-calculated trajectories, graph-based exploration, and active loop-closing to enable rapid, accurate large-scale 3D mapping while ensuring obstacle avoidance and improved pose estimation.

Contribution

It presents a new exploration approach integrating Peacock Trajectory, graph exploration, and active loop-closing for efficient large-scale 3D mapping with UAVs.

Findings

Outperforms state-of-the-art methods in simulation environments.

Successfully validated in real-world UAV experiments.

Achieves faster exploration with improved map accuracy.

Abstract

Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescues, deliveries, and so forth. Therefore, unmanned aerial vehicles (UAVs), which are fast, agile, and have high degrees of freedom, have been widely used. However, previous approaches have two limitations: a) First, they may not be appropriate for exploring large-scale environments because they mainly depend on random sampling-based path planning that causes unnecessary movements. b) Second, they assume the pose estimation is accurate enough, which is the most critical factor in obtaining an accurate map. In this paper, to explore and map unknown large-scale environments rapidly and accurately, we propose a novel exploration method that combines the pre-calculated Peacock Trajectory with graph-based global exploration and active loop-closing. Because the two-step trajectory that considers the kinodynamics of UAVs is used, obstacle avoidance is guaranteed in the receding-horizon manner. In addition, local exploration that considers the frontier and global exploration based on the graph maximizes the speed of exploration by minimizing unnecessary revisiting. In addition, by actively closing the loop based on the likelihood, pose estimation performance is improved. The proposed method's performance is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.