The fusion of Global Navigation Satellite System (GNSS) and Light Detection and Ranging (LiDAR) sensors has emerged as a critical research area for high-precision navigation and mapping applications. While GNSS provides absolute positioning, it is susceptible to multipath errors, signal occlusions, and atmospheric disturbances. LiDAR, on the other hand, offers high-resolution environmental perception but lacks absolute localization and is sensitive to sensor noise and drift over time. To address these limitations, robust sensor fusion architectures are necessary to improve positioning accuracy, reliability, and robustness in diverse environments.
This research focuses on the systematic modeling of GNSS and LiDAR errors to enhance sensor fusion performance. A key aspect of this work is the design of fusion architectures that optimize trade-offs between accuracy, environmental-dependency, and robustness to sensor failures. To this end, this research investigates trajectory alignment, geometric similarity and sensor signal dropouts. Various fusion strategies, including tightly coupled and loosely coupled approaches, are explored to evaluate their effectiveness under different operational conditions.
Simulation-based evaluation is a core component of this study, enabling controlled analysis of sensor errors, fusion methodologies, and performance metrics. A custom Blender-based simulation framework has been developed to facilitate reproducible experiments and allow for benchmarking of different fusion strategies. By systematically analyzing fusion performance in terms of accuracy, consistency, and computational cost, this work aims to provide valuable insights into the optimal integration of GNSS and LiDAR for real-world applications. The simulation framework generates a reusable output format in order to demonstrate the flexibility of this methodology by running a selected fusion approach on real data (Sim2Real).
The proposed framework and findings contribute to the research community by providing tools and methodologies for evaluating sensor fusion strategies, fostering advancements in precise and resilient localization solutions for autonomous systems, robotics, and geospatial applications in challenging environments.