Point-Cloud Filtering Algorithm for Port-Environment Perception Based on 128-Line Array Single-Photon LiDAR

Light detection and ranging (LiDAR) has been widely used in navigation and environmental perception owing to its excellent beam directivity and high spatial resolution. Among its modalities, single-photon (photon-counting) LiDAR offers higher detection sensitivity at long ranges and under weak-return conditions and has therefore attracted considerable attention. However, this high sensitivity also introduces substantial background counts into the raw measurements; without effective filtering, downstream tasks such as image reconstruction and target recognition are hindered. In this work, a 128-line single-photon LiDAR system for port-environment perception was designed, and a histogram-based statistical filtering engineering solution was proposed. The algorithm incorporates distance-based piecewise adaptive parameterization and adjacent-channel fusion while maintaining a small memory footprint and facilitating deployment. Field experiments using datasets collected in Qingdao and Shanghai demonstrated good denoising performance at ranges up to 2.4 km. In simulation experiments using synthetic data with ground truth, an F1 score of 0.9091 was achieved by RA-ACF HSF, outperforming the baseline methods DBSCAN (0.6979) and ROR (0.7500). The proposed system and method provide a practical engineering solution for maritime navigation and port-environment perception.