Safe Distance Monitoring for Substation Near-Current Operations via Image–LiDAR Cross-Modal Self-Registration

Continuous monitoring of the minimum safety distance between construction machinery and energized bodies is essential during operations near energized equipment in substations. Existing methods mostly rely on fixed-view observation, online LiDAR, or rigid camera–LiDAR installation, leading to inflexible deployment, high extrinsic-maintenance cost, and insufficient metric consistency across viewpoints. To address these limitations, this paper proposes a safety-distance monitoring method based on cross-modal self-registration between monocular images and a pre-built LiDAR map. During online operation, only the current monocular image is used. Monocular depth estimation first generates a pseudo-point cloud, which is then registered with historical LiDAR point clouds to solve the camera pose and align the current observation with the map. Combined with target-boundary segmentation and prior energized-hazardous-region information, the method localizes key parts of construction machinery in 3D and computes the minimum safety distance to energized regions. Experiments show that the proposed method achieves a registration recall of 92.2%, a mean absolute error of 0.748 m, a maximum error of 0.822 m, and a single-frame latency of 180 ms. Notably, these results are achieved without real-time LiDAR input or on-site extrinsic recalibration. These results demonstrate the feasibility of the proposed framework in a representative substation scenario and indicate its potential for auxiliary online safety-distance monitoring.