Cluster-ALIV: Aerial LiDAR-Inertia-Visual Dense Reconstruction for Cluster UAV

Abstract

Unmanned aerial vehicles (UAVs) equipped with LiDAR, camera, and Inertial Measurement Unit sensors are increasingly utilized for real-time dense reconstruction in large-scale rescue operations and environmental monitoring, among others. However, achieving algorithmic robustness remains challenging due to the UAVs' high-speed flight and rapid pose changes. Additionally, energy constraints on individual UAVs can be mitigated through multi-UAV collaboration, improving operational efficiency. Nevertheless, when faced with unknown environments or the loss of Global Navigation Satellite System signal, most multi-UAV dense reconstruction systems can't work, making it hard to construct a global consistent map. In this paper, we propose Cluster-ALIV, a real-time dense reconstruction system for multiple UAVs that effectively supports aerial, large-scale scenarios with lost global positioning and weak co-visibility of LiDAR or vision. The system integrates LiDAR-Inertial-Visual odometry through multi-sensor fusion to generate accurate, gravity-aligned, colorized LiDAR point clouds and visual information with scale. Overall, in the Cluster-ALIV, each UAV executes a LiDAR-Inertial-Visual odometry, transmitting point cloud and visual data to a ground server, where multi-UAV joint optimization is performed through LiDAR post-processing, visual post-processing, and normal distributions transform refinement. Extensive experiments demonstrate that our system can efficiently construct large-scale dense map in real time with high accuracy and robustness.