Uncertainty-aware Evaluation and Fusion of Point Clouds for Simultaneous Scanning of Two State-of-the-art Indoor MLS Systems

Abstract. In recent years, Mobile Laser Scanning (MLS) systems have garnered increasing attention for their efficient and convenient data acquisition, finding widespread applications across various fields. However, most users typically select a single system that best suits their needs, with few attempts to utilize two independent MLS systems simultaneously. To explore this possibility, we selected two currently leading commercial MLS systems (Z+F FlexScan 22 and Leica BLK ARC) and developed an uncertainty analysis workflow to quantitatively evaluate their respective uncertainties. Following this, a trajectory-based fusion method was employed to merge point clouds from both systems. The experimental results demonstrate that our uncertainty-aware evaluation and fusion solution successfully decreased trajectory estimation drift and point cloud noise, offering a feasible solution for evaluation and fusion of simultaneous scanning with dual indoor MLS systems.