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北斗三号辅助山区公路地形数据采集与处理技术研究

Study on terrain acquisition and processing technology of BDS-3 auxiliary mountain highway.

作者信息

Lin Guangtai, Li Shijian, Wang Jianjun, Li Yongyou, Qin Jingjun, Yan Rong

机构信息

Guangxi Road and Bridge Engineering Group Co., LTD, Nanning, China.

Guangxi Road and Bridge Engineering Group Co., LTD. BIM technology research center, Nanning, 530000, China.

出版信息

Sci Rep. 2024 Oct 23;14(1):24991. doi: 10.1038/s41598-024-74877-5.

DOI:10.1038/s41598-024-74877-5
PMID:39443606
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11500386/
Abstract

The operation mode of UAV equipped with LiDAR can significantly reduce the efficiency and cost of highway terrain acquisition. At present, due to the complicated terrain and weak signal of mountain road, it is difficult to collect terrain data. In view of the above problems, this paper puts forward the technology of BDS assisted terrain acquisition and processing on complex mountain roads. This paper describes in detail the new signal unfuzzy acquisition and tracking algorithm and multi-antenna positioning and attitude determination technology and equipment of BDS-3 mountain highway, through millimeter wave Lidar altimetry technology, imitation ground flight, air-ground multi-source data combined with air three-direction method and other technologies. To realize efficient 3D collection and 3D reconstruction of massive original images of terrain of complex mountain traffic lines in the range of 100 km. Finally, a case study of 100 km mountain road is taken to verify the effectiveness of the method.

摘要

配备激光雷达的无人机作业模式可显著降低公路地形采集的效率和成本。目前,由于山路地形复杂、信号微弱,地形数据采集困难。针对上述问题,本文提出了复杂山区道路北斗卫星导航系统(BDS)辅助地形采集与处理技术。本文详细介绍了BDS-3山区公路新的信号解模糊采集与跟踪算法以及多天线定位与姿态确定技术和设备,通过毫米波激光雷达测高术、模拟地面飞行、空-地多源数据结合空三方向法等技术,实现100公里范围内复杂山区交通线路地形海量原始图像的高效三维采集与三维重建。最后,以100公里山路为例进行案例研究,验证该方法的有效性。

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Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook.机器学习为高熵化合物探索铺平道路:挑战、进展与展望。
Adv Mater. 2023 Sep 9:e2305192. doi: 10.1002/adma.202305192.
3
Understanding the evolution of lithium dendrites at LiAlLaZrO grain boundaries via operando microscopy techniques.通过在位显微镜技术理解 LiAlLaZrO 晶粒边界处锂枝晶的演变。
Nat Commun. 2023 Mar 9;14(1):1300. doi: 10.1038/s41467-023-36792-7.
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Machine learning of material properties: Predictive and interpretable multilinear models.材料性能的机器学习:预测性和可解释的多线性模型。
Sci Adv. 2022 May 6;8(18):eabm7185. doi: 10.1126/sciadv.abm7185.
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Predicting materials properties without crystal structure: deep representation learning from stoichiometry.无需晶体结构预测材料属性:基于化学计量学的深度表征学习
Nat Commun. 2020 Dec 8;11(1):6280. doi: 10.1038/s41467-020-19964-7.