Dokon Kim
High-level autonomous driving requires an exceptionally accurate and robust perception of surrounding road environments to support reliable vehicle localization. While utilizing multiple LiDAR sensors expands the field of view and provides high-density point clouds, it introduces severe challenges, such as multi-LiDAR mutual interference (crosstalk) and significant computational overhead. This paper proposes a multi-stage front-end perception pipeline designed for robust high-altitude structural landmark extraction and crosstalk suppression to provide clean geometric reference points for downstream positioning systems. The proposed framework first employs a Binary Bayes Filter combined with a mesh-graph representation to segment stable overhead road traffic signs while minimizing environmental noise. To ensure real-time operation, a hybrid cascade consisting of 2D-grid spatial projection and voxelized 3D Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is introduced, drastically reducing point cloud density bottlenecks. Finally, a cluster-refinement stage is applied to filter out residual false positives induced by crosstalk. The system was validated using real-world driving data collected over an urban sequence (1.88 km) and a highway sequence (37 km). Experimental results demonstrate that the pipeline achieves high landmark tracking success rates of 79.9% and 98.5%, respectively, while suppressing crosstalk-induced false alarms down to 12.3%. Operating entirely on CPU threads with an average latency of 24.5 ms per frame, the framework satisfies real-time execution bounds for standard 10 Hz LiDAR setups, establishing a high-fidelity front-end capable of preventing tracking drift in autonomous vehicle localization.