Airborne LIDAR system for real-time power lines recognition and terrain assessment

High speed flying drones and helicopters poses a significant flight safety risk due to the potential for collision with power lines and uneven landing grounds. There are few reports on light detection and ranging (LIDAR) systems for high-speed flight platforms. This study established an airborne, high-resolution light detection and ranging LIDAR system integrating a dual-wavelength laser source, a multi-beam transceiver scanning device, a two-dimensional mirror, and micro-electro-mechanical system (MEMS) scanning technology. Furthermore, the system achieves high-precision calibration with navigation systems by employing a voxel minimization strategy and a least squares fitting algorithm. It was compared with the performance of height-based clustering (k-means) and Hough transform and an improved point pillars convolutional neural network algorithm in power line recognition. The LIDAR system was tested on a high-speed helicopter platform reaching speeds of 120 km/h, enabling real-time recognition of power lines. Terrain assessment plays an important role in aircraft landing. The random sample consensus (RANSAC) method was used to extract ground points from the point cloud in real time at a rate of 5 ms per scan, ensuring terrain inclination estimation with minimal latency. This research provides an effective solution for real-time power line recognition and terrain assessment for flight platforms, thereby enhancing flight safety.