Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds

Y et, despite this progress, the point clouds acquired by MLS systems operating in real-world environments inevitably contain uncertainty arising from various error sources during acquisition and processing. Although MLS systems have advanced rapidly in both data collection and post-processing, research on uncertainty evaluation has received comparatively less attention and remains underdeveloped (Xu et al., 2025b). From a user's perspective, the quality of point clouds from MLS systems is a critical concern. As the foundational input for many downstream tasks, inadequate assessment of MLS point clouds' quality can easily impact high-precision applications such as navigation and change analysis. This will not only undermine reliability but also result in substantial waste of time and resources, which is unacceptable in real-world applications. There is a clear need for automated and reliable solutions for uncertainty evaluation. In MLS systems, four main categories of error sources contribute to uncertainty: instrumental errors, atmospheric errors, object-and geometry-related errors, and trajectory estimation errors (Habib et al., 2009, Schenk, 2001). Considering the characteristics of these error sources, existing uncertainty evaluation methods can be broadly divided into two categories: forward modeling and backward modeling (Shi et al., 2021). The core idea of forward modeling is grounded in variance-covariance propagation, which involves detailed theoretical analysis of MLS system errors.