Lidar-PTZ Fusion System for Environmental Perception and Analysis Detection

초록

With the rapid proliferation of unmanned aerial vehicles (UAVs), concerns regarding privacy breaches, unauthorized intrusions, and public safety have become increasingly critical. To address these challenges, this study developed a lidar-pan-tilt-zoom (PTZ) camera fusion system for real-time UAV detection and tracking. The proposed approach integrates YOLOv11-based visual detection, PointPillars point-cloud processing, and an adaptive extended Kalman filter (EKF) within a unified framework to achieve robust and temporally consistent target state estimation under sparse observation conditions. A spatially constrained virtual-point augmentation scheme is introduced to improve feature stability and model generalization under sparse lidar point-cloud conditions. Furthermore, a dual-layer fusion EKF strategy is designed to jointly exploit PointPillars detection outputs and adaptive noise-aware centroid measurements, enabling robust dynamic state estimation and continuous tracking even with intermittent data. The proposed system was implemented and evaluated through both public data sets and real-world outdoor experiments. Experimental results demonstrate that the lidar-PTZ fusion system achieves an average positional deviation of +/- 1.62 m, an effective detection range of about 30 m, and a maximum valid range of approximately 42 m, while maintaining a frame-level detection continuity of 96.8% and operating at around 32 frames/second. The evaluation results demonstrate that the fusion strategy effectively mitigates the limitations of single-sensor detection and provides a reliable foundation for multi-target cooperative sensing and airspace security monitoring applications.