Evaluation of 3D ROS-based Simultaneous Localization and Mapping Under Noisy Conditions for Uncrewed Surface Vessels Navigation
K. Li · 2025
Accurate mapping is crucial for the safe operation of Uncrewed Surface Vessels (USV), especially when relying on 3D LiDAR sensors, which are susceptible to noise from factors like water spray and turbulent surface conditions. This paper investigates the performance of two prominent 3D LiDAR mapping algorithms---HDL graph SLAM and Point LIO---when subjected to injected noise models that mimic real-world maritime disturbances. By introducing controlled noise into the sensor data, we evaluate how each algorithm maintains mapping accuracy under degraded conditions. Quantitative metrics such as Structural Similarity Index Measure (SSIM) and Intersection over Union (IoU) are utilized to assess and compare the robustness of the generated maps. The study will highlight the open-source ROS mapping algorithms strengths and limitations in noisy environments. These findings aim to inform the development of more robust mapping strategies, ultimately enhancing the reliability and safety of USV navigation in challenging maritime settings.