SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction

TL;DR

SOAR is a multi-UAV system combining LiDAR and visual sensors, employing exploration and optimized viewpoint planning to enable fast, autonomous scene reconstruction in complex environments.

Contribution

The paper introduces a novel heterogeneous UAV system with a frontier-based exploration and viewpoint optimization strategy for efficient autonomous reconstruction.

Findings

Outperforms classical methods in simulation benchmarks.

Efficiently explores complex environments with heterogeneous UAVs.

Achieves rapid scene surface reconstruction with optimized planning.

Abstract

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in scene reconstruction. This paper presents SOAR, a LiDAR-Visual heterogeneous multi-UAV system specifically designed for fast autonomous reconstruction of complex environments. Our system comprises a LiDAR-equipped explorer with a large field-of-view (FoV), alongside photographers equipped with cameras. To ensure rapid acquisition of the scene's surface geometry, we employ a surface frontier-based exploration strategy for the explorer. As the surface is progressively explored, we identify the uncovered areas and generate viewpoints incrementally. These viewpoints are then assigned to photographers through solving a Consistent Multiple Depot Multiple Traveling Salesman Problem (Consistent-MDMTSP), which optimizes scanning efficiency while ensuring task consistency. Finally, photographers utilize the assigned viewpoints to determine optimal coverage paths for acquiring images. We present extensive benchmarks in the realistic simulator, which validates the performance of SOAR compared with classical and state-of-the-art methods. For more details, please see our project page at https://sysu-star.github.io/SOAR}{sysu-star.github.io/SOAR.