ORIGINAL RESEARCH article
Front. Phys.
Sec. Optics and Photonics
This article is part of the Research TopicAcquisition and Application of Multimodal Sensing Information, Volume IIIView all 5 articles
Airborne LIDAR system for real-time power lines recognition and terrain assessment
Provisionally accepted- 1Xidian University, Xi'an, China
- 2China Electronics Technology Group Corporation 27th Research Institute, Zhengzhou, China
- 3Xi'an Jiaotong University, Xi'an, China
- 4Songshan Laboratory, Zheng zhou, China
- 5Wuhan University, Wuhan, China
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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 5ms 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.
Keywords: lidar system, High-speed airborne platform, Voxel minimization, Point pillars, Terrain assessment, Real-time recognition
Received: 10 Oct 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Zhang, Liu, Wang, Peng, Li, Zhao, Zhang, Wu, Liu, Zhao and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chi Zhang, zhangchixidian@stu.xidian.edu.cn
Maliang Liu, mlliu@xidian.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
