Uncertainty-Aware LiDAR Object Registration Algorithm for Urban Semantic Mapping

초록

LiDAR directly acquires 3D point measurements of physical surfaces, making it well-suited for interpreting objects in tasks such as object reconstruction and semantic mapping. However, each object is represented by a sparse point set, and the observations are inherently partial due to self-occlusion, which poses significant challenges for object registration. The uncertainty of the modeled surface depends on the local point density, and outliers from non-overlapping areas are often involved in the registration process. To address these issues, we propose a stochastic object registration method that models surface uncertainty using a Gaussian process and an overlap determination scheme based on object detector outputs. Both the overlap determination scheme and the stochastic object registration method are verified through Monte Carlo simulations on numerous real-world urban object point clouds, demonstrating performance improvements of 52% and 12%, respectively, compared to the baseline methods. To validate the full pipeline in real-world conditions, the proposed registration method is integrated with PointPillars, achieving an 8% accuracy improvement over the baseline method and demonstrating a 98.8% convergence ratio in LiDAR-based vehicle registration.

키워드