OwlFusion: Depth-Only Onboard Real-Time 3D Reconstruction of Scalable Scenes for Fast-Moving MAV

Abstract

Real-time 3D reconstruction combined with MAVs has garnered significant attention in a variety of fields, including building maintenance, geological exploration, emergency rescue, and cultural heritage protection. While MAVs possess the advantages of speed and lightness, they also exhibit strong image blur and limited computational resources. To address these limitations, this paper presents a novel approach for onboard, depth-only, real-time 3D reconstruction capable of accommodating fast-moving MAVs. Our primary contribution is a dense SLAM system that combines surface hierarchical sparse representation and particle swarm pose optimization. Our system enables the robust tracking of high-speed camera motion and facilitates scaling to large scenes without being constrained by GPU memory resources. Our robust camera tracking framework is capable of accommodating fast camera motions and varying environments solely by relying on depth images. Furthermore, by integrating path planning methods, we explore the capabilities of MAV autonomous mapping in unknown environments with restricted lighting. Our efficient reconstruction system is capable of generating highly dense point clouds with resolutions ranging from 2 mm to 8 mm on surfaces of different complexities at rates approaching 30 Hz, fully onboard a MAV. We evaluate the performance of our method on both datasets and real-world platforms and demonstrate its superior accuracy and efficiency compared to existing methods.

Keywords

• onboard 3D reconstruction
• micro aerial vehicles (MAVs)
• RGB-D SLAM
• fast camera tracking
• particle swarm pose optimization
• hierarchical sparse 3D representation