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Article(id=1322164827104063606, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164760943116291, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736265600000, receivedDateStr=2025-01-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793158259416, onlineDateStr=2026-10-28, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793158259416, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793158259416, creator=13701087609, updateTime=1793158259416, updator=13701087609, issue=Issue{id=1322164760943116291, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='12', pageStart='1', pageEnd='302', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158243641, creator='13701087609', updateTime=1793158929367, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322167637195477357, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164760943116291, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322167637199671662, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164760943116291, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=264, endPage=270, ext={EN=ArticleExt(id=1322164827322167415, articleId=1322164827104063606, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Comparative Study on the Surveying and Mapping Accuracy and Operation Efficiency of UAV in Non-Coal Open-Pit Mines with Large Height Difference, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In the field of UAV-based open-pit mine surveying, most previous studies adopted only one type of UAV for data collection and analysis, without considering the issue of internal and external operation efficiency. Therefor, taking Xiangxiang Dolomite Mine as the engineering background, two types of UAVs, namely Matrice 300 RTK and Mavic 3E, were adopted. External data collection with four different flight heights was carried out in the mining area. In the internal data processing, four image control point layout schemes were selected respectively under each working condition to analyze the measuring point accuracy under different flight heights and different numbers of image control points. Meanwhile, statistical analysis of the internal and external time-consuming under each working condition was conducted, and the surveying operation efficiency of UAV-based open-pit mine was analyzed. The results show that the overall accuracy of the M300 RTK is higher than that of the Mavic 3E under the same working conditions, and as the flight heights increase, the accuracy continuously decreases. Under the working conditions of the same flight height, as the number of image control points increases, the accuracy of the two types of UAVs continues to improve, but the improvement range is not significant. Under the working conditions of the same flight height, the external time-consuming of the two types of UAVs is basically consistent, but the internal time-consuming of the Mavic 3E is only 3.8％ to 8.8％ of that of the M300 RTK. In non-coal open-pit mines with large height difference, the adoption of the Mavic 3E's real-time terrain-following flight combined with the image-control-free mode not only eliminates the field operation step of laying out image control points but also significantly reduces internal time-consuming. While ensuring accuracy, it can greatly improve the surveying operation efficiency of UAVs in mines.

, authors=Zhiwei WU1, Wenkai LIAO2, Jincai TANG3, Ying WU4, authorsList=Zhiwei WU, Wenkai LIAO, Jincai TANG, Ying WU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322164831252230282, articleId=1322164827104063606, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=大高差非煤露天矿山无人机测绘精度及效率对比研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

在无人机露天矿山测量领域，以往的研究大多选用一种机型无人机进行数据采集和分析，且未考虑内、外业作业效率问题。为此以湘乡白云石矿为工程背景，采用大疆经纬M300 RTK和Mavic 3E两种机型无人机，对矿区开展了4种航高的外业数据采集，内业数据处理中各工况分别选取4种像控点布设方案，分析不同航高、不同像控点数量下的测点精度；同时，统计各工况下的内、外业耗时，进而对无人机露天矿山测量效率进行分析。结果表明：M300 RTK的整体精度高于同工况Mavic 3E，随着航高的增加，精度不断降低；在相同航高工况下，随着像控点数量的增加，两种机型无人机的精度不断提高，但提高的幅度不大；在相同航高工况下，两种机型无人机的外业耗时基本一致，但Mavic 3E的内业耗时仅为M300 RTK的3.8％~8.8％；在大高差非煤露天矿山采用Mavic 3E的实时仿地飞行配合免像控模式，既可免除外业布设像控点的作业步骤，又使得内业耗时大幅减少，在保证精度的情况下，可大幅提高矿山无人机测绘作业效率。

, authors=武志伟1, 廖文凯2, 唐金才3, 伍英4, authorsList=武志伟, 廖文凯, 唐金才, 伍英, authorCompany=null, correspAuthors=null, authorNote=

武志伟（1985—），男，湖南郴州人，硕士，讲师，主要从事矿山测量和矿山安全方面的研究。E-mail：

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武志伟（1985—），男，湖南郴州人，硕士，讲师，主要从事矿山测量和矿山安全方面的研究。E-mail：

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武志伟（1985—），男，湖南郴州人，硕士，讲师，主要从事矿山测量和矿山安全方面的研究。E-mail：

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Research on calculation model of soil and rock volume of mine ecological restoration using aerial survey data[J]. Bulletin of Surveying and Mapping, 2023 (10): 129-134., articleTitle=Research on calculation model of soil and rock volume of mine ecological restoration using aerial survey data, refAbstract=null)], funds=[Fund(id=1322164841800904908, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, awardId=24C0950, language=CN, fundingSource=湖南省教育厅科学研究项目(24C0950), fundOrder=null, country=null), Fund(id=1322164841888985293, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, awardId=CG-ZDJH20241030, language=CN, fundingSource=湘潭市科技创新项目(CG-ZDJH20241030), fundOrder=null, country=null), Fund(id=1322164841985454286, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, awardId=2024HNVITZK003, language=CN, fundingSource=湖南理工职业技术学院自科重点课题(2024HNVITZK003), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322164831621329035, tenantId=1146029695717560320, 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remark=3.Xiangxiang Dolomite Mine of Xiangtan Iron & Steel Group Co., Ltd., Xiangtan, Hunan 411423, China), AuthorCompanyExt(id=1322164831910736019, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, companyId=1322164831889764497, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.湘潭钢铁集团有限公司湘乡白云石矿，湖南 湘潭市　411423)]), AuthorCompany(id=1322164832007205012, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, xref=4., ext=[AuthorCompanyExt(id=1322164832023982229, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, companyId=1322164832007205012, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.Guangdong Ruidong Survey Foundation Engineering Co., Ltd., Dongguan, Guangdong 523000, China), AuthorCompanyExt(id=1322164832049148054, 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caption=矿山局部三维模型, figureFileSmall=I7dE3ob3aN7NmvqVHQcs/A==, figureFileBig=IVv4+rMdgdhxlGPCCd9Tzw==, tableContent=null), ArticleFig(id=1322164840655859904, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Fig.6, caption=Three-dimensional mean square error histogram of the model, figureFileSmall=JBMwRiDSfMViC4hO5dyITQ==, figureFileBig=lg/ISJIwwwICLhxrwMHySw==, tableContent=null), ArticleFig(id=1322164840735551681, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=图6, caption=模型三维中误差柱状图, figureFileSmall=JBMwRiDSfMViC4hO5dyITQ==, figureFileBig=lg/ISJIwwwICLhxrwMHySw==, tableContent=null), ArticleFig(id=1322164840802660546, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Table 1, caption=

UAV parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
机型空机质量/kg最大起飞质量/kg最大飞行速度/（m/s）抗风能力/（m/s）悬停精度/m最大飞行时间/min最大测控半径/km
M300 RTK6.3（双电池）9.0017（P模式）12±0.15515
Mavic 3E0.9151.0515（P模式）12±0.14515
), ArticleFig(id=1322164840903323843, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=表1, caption=

无人机参数

, figureFileSmall=null, figureFileBig=null, tableContent=
机型空机质量/kg最大起飞质量/kg最大飞行速度/（m/s）抗风能力/（m/s）悬停精度/m最大飞行时间/min最大测控半径/km
M300 RTK6.3（双电池）9.0017（P模式）12±0.15515
Mavic 3E0.9151.0515（P模式）12±0.14515
), ArticleFig(id=1322164841012375748, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Table 2, caption=

Coordinates of image control points

, figureFileSmall=null, figureFileBig=null, tableContent=
点号X/mY/mZ/m
1**71 471.109**5 958.937118.977
2**71 638.543**5 849.020123.970
3**71 917.180**5 863.666129.072
4**72 251.778**6 124.205120.358
5**72 287.933**6 372.316120.866
6**72 111.830**6 604.678110.856
7**71 733.500**6 722.71896.099
8**71 650.361**6 240.789160.670
9**71 805.219**6 101.625183.505
10**71 982.460**6 198.658209.807
), ArticleFig(id=1322164841092067525, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=表2, caption=

像控点坐标

, figureFileSmall=null, figureFileBig=null, tableContent=
点号X/mY/mZ/m
1**71 471.109**5 958.937118.977
2**71 638.543**5 849.020123.970
3**71 917.180**5 863.666129.072
4**72 251.778**6 124.205120.358
5**72 287.933**6 372.316120.866
6**72 111.830**6 604.678110.856
7**71 733.500**6 722.71896.099
8**71 650.361**6 240.789160.670
9**71 805.219**6 101.625183.505
10**71 982.460**6 198.658209.807
), ArticleFig(id=1322164841205313734, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Table 3, caption=

Image control points participating in the solution

, figureFileSmall=null, figureFileBig=null, tableContent=
像控点数量/个参与解算像控点
0—
3XK1、XK4、XK7
5XK1、XK3、XK5、XK7、XK8
7XK1、XK3、XK4、XK5、XK6、XK7、XK8
), ArticleFig(id=1322164841322754247, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=表3, caption=

参与解算的像控点

, figureFileSmall=null, figureFileBig=null, tableContent=
像控点数量/个参与解算像控点
0—
3XK1、XK4、XK7
5XK1、XK3、XK5、XK7、XK8
7XK1、XK3、XK4、XK5、XK6、XK7、XK8
), ArticleFig(id=1322164841415028936, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Table 4, caption=

Plane mean square error and three-dimensional mean square error of the checkpoints

, figureFileSmall=null, figureFileBig=null, tableContent=
航高像控点数量/个M300 RTKMavic 3E
ms/cmm3D/cmms/cmm3D/cm
仿地飞行100 m04.85.38.69.2
34.14.87.28.2
53.54.46.87.7
73.24.04.75.8
定高飞行100 m05.27.98.510.3
35.26.88.49.5
54.55.88.08.2
73.75.77.28.9
定高飞行150 m06.47.511.013.4
35.47.010.312.4
55.26.69.110.1
74.86.17.89.2
定高飞行200 m06.48.513.914.8
36.27.813.013.7
55.77.311.412.7
75.56.89.39.5
), ArticleFig(id=1322164841494720713, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=表4, caption=

检查点平面中误差和三维中误差

, figureFileSmall=null, figureFileBig=null, tableContent=
航高像控点数量/个M300 RTKMavic 3E
ms/cmm3D/cmms/cmm3D/cm
仿地飞行100 m04.85.38.69.2
34.14.87.28.2
53.54.46.87.7
73.24.04.75.8
定高飞行100 m05.27.98.510.3
35.26.88.49.5
54.55.88.08.2
73.75.77.28.9
定高飞行150 m06.47.511.013.4
35.47.010.312.4
55.26.69.110.1
74.86.17.89.2
定高飞行200 m06.48.513.914.8
36.27.813.013.7
55.77.311.412.7
75.56.89.39.5
), ArticleFig(id=1322164841582801098, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=EN, label=Table 5, caption=

Statistics of time-consuming of internal and external work

, figureFileSmall=null, figureFileBig=null, tableContent=
航高像控点数量/个像控点耗时/minM300 RTKMavic 3E
外业耗时/min内业耗时/min外业耗时/min内业耗时/min
仿地飞行100 m00732 09172183
336
560
784
定高飞行100 m00672 12970155
336
560
784
定高飞行150 m00621 7845068
336
560
784
定高飞行200 m00468584146
336
560
784
), ArticleFig(id=1322164841666687179, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164827104063606, language=CN, label=表5, caption=

内、外业耗时统计

, figureFileSmall=null, figureFileBig=null, tableContent=
航高像控点数量/个像控点耗时/minM300 RTKMavic 3E
外业耗时/min内业耗时/min外业耗时/min内业耗时/min
仿地飞行100 m00732 09172183
336
560
784
定高飞行100 m00672 12970155
336
560
784
定高飞行150 m00621 7845068
336
560
784
定高飞行200 m00468584146
336
560
784
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大高差非煤露天矿山无人机测绘精度及效率对比研究
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武志伟 1 , 廖文凯 2 , 唐金才 3 , 伍英 4
矿业研究与开发 | 矿山机电与矿业智能化 2025,45(12): 264-270
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矿业研究与开发 |矿山机电与矿业智能化 2025 , 45 (12) : 264 -270
大高差非煤露天矿山无人机测绘精度及效率对比研究
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武志伟1 , 廖文凯2, 唐金才3, 伍英4
作者信息
  • 1.湖南理工职业技术学院，湖南 湘潭市　411104
  • 2.长沙市光启勘测工程技术有限公司，湖南 长沙　410114
  • 3.湘潭钢铁集团有限公司湘乡白云石矿，湖南 湘潭市　411423
  • 4.广东瑞东勘察基础工程有限公司，广东 东莞市　523000
作者简介:

武志伟（1985—），男，湖南郴州人，硕士，讲师，主要从事矿山测量和矿山安全方面的研究。E-mail：

Comparative Study on the Surveying and Mapping Accuracy and Operation Efficiency of UAV in Non-Coal Open-Pit Mines with Large Height Difference
Zhiwei WU1 , Wenkai LIAO2, Jincai TANG3, Ying WU4
Affiliations
  • 1.Hunan Vocational Institute of Technology, Xiangtan, Hunan 411104, China
  • 2.Changsha Guangqi Survey Engineering Technology Co., Ltd., Changsha, Hunan 410114, China
  • 3.Xiangxiang Dolomite Mine of Xiangtan Iron & Steel Group Co., Ltd., Xiangtan, Hunan 411423, China
  • 4.Guangdong Ruidong Survey Foundation Engineering Co., Ltd., Dongguan, Guangdong 523000, China
出版时间: 2025-12-25
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在无人机露天矿山测量领域，以往的研究大多选用一种机型无人机进行数据采集和分析，且未考虑内、外业作业效率问题。为此以湘乡白云石矿为工程背景，采用大疆经纬M300 RTK和Mavic 3E两种机型无人机，对矿区开展了4种航高的外业数据采集，内业数据处理中各工况分别选取4种像控点布设方案，分析不同航高、不同像控点数量下的测点精度；同时，统计各工况下的内、外业耗时，进而对无人机露天矿山测量效率进行分析。结果表明：M300 RTK的整体精度高于同工况Mavic 3E，随着航高的增加，精度不断降低；在相同航高工况下，随着像控点数量的增加，两种机型无人机的精度不断提高，但提高的幅度不大；在相同航高工况下，两种机型无人机的外业耗时基本一致，但Mavic 3E的内业耗时仅为M300 RTK的3.8％~8.8％；在大高差非煤露天矿山采用Mavic 3E的实时仿地飞行配合免像控模式，既可免除外业布设像控点的作业步骤，又使得内业耗时大幅减少，在保证精度的情况下，可大幅提高矿山无人机测绘作业效率。

露天矿山  /  大高差  /  无人机  /  测绘精度  /  作业效率

In the field of UAV-based open-pit mine surveying, most previous studies adopted only one type of UAV for data collection and analysis, without considering the issue of internal and external operation efficiency. Therefor, taking Xiangxiang Dolomite Mine as the engineering background, two types of UAVs, namely Matrice 300 RTK and Mavic 3E, were adopted. External data collection with four different flight heights was carried out in the mining area. In the internal data processing, four image control point layout schemes were selected respectively under each working condition to analyze the measuring point accuracy under different flight heights and different numbers of image control points. Meanwhile, statistical analysis of the internal and external time-consuming under each working condition was conducted, and the surveying operation efficiency of UAV-based open-pit mine was analyzed. The results show that the overall accuracy of the M300 RTK is higher than that of the Mavic 3E under the same working conditions, and as the flight heights increase, the accuracy continuously decreases. Under the working conditions of the same flight height, as the number of image control points increases, the accuracy of the two types of UAVs continues to improve, but the improvement range is not significant. Under the working conditions of the same flight height, the external time-consuming of the two types of UAVs is basically consistent, but the internal time-consuming of the Mavic 3E is only 3.8％ to 8.8％ of that of the M300 RTK. In non-coal open-pit mines with large height difference, the adoption of the Mavic 3E's real-time terrain-following flight combined with the image-control-free mode not only eliminates the field operation step of laying out image control points but also significantly reduces internal time-consuming. While ensuring accuracy, it can greatly improve the surveying operation efficiency of UAVs in mines.

Open-pit mine  /  Large height difference  /  UAV  /  Surveying and mapping accuracy  /  Operation efficiency
武志伟, 廖文凯, 唐金才, 伍英. 大高差非煤露天矿山无人机测绘精度及效率对比研究. 矿业研究与开发, 2025 , 45 (12) : 264 -270 .
Zhiwei WU, Wenkai LIAO, Jincai TANG, Ying WU. Comparative Study on the Surveying and Mapping Accuracy and Operation Efficiency of UAV in Non-Coal Open-Pit Mines with Large Height Difference[J]. Mining Research and Development, 2025 , 45 (12) : 264 -270 .
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0　引言
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非煤露天矿山因其具有开采规模大、基建时间短、机械化程度高、生产效率高和作业条件好等优势，在我国矿产资源开发中占据重要地位。截至2023年3月，全国非煤露天矿山约有2万座[1]，其开采方式主要采用自上而下台阶式开采，台阶高度一般在12~15 m，设计的台阶坡面角一般在65°~70°，由于地质条件差异及爆破控制等因素，现场台阶坡面角一般均大于设计值，局部甚至达到80°。矿山在生产、储量评估、生态修复及绿色矿山建设等过程中均需对开采现状进行测绘，常规的全站仪测绘技术、实时动态测量技术等需临坡进行作业，易造成高处坠落等生产安全事故，且工作效率不高。无人机（UAV）倾斜摄影测量技术能够通过非接触式测量快速获取现场数据，通过后期数据处理，能够得到矿区数字正射影像图、数字高程模型、数字线划图及三维模型数据，该技术具有高效、便捷、低成本等优势。周吕等[2]、黄军等[3]、李博等[4]、李卫军等[5]、王成等[6]、谢君洋等[7]对矿山资源量动态监测及土石方量计算进行了研究，结果表明，数据误差满足规范要求，可为矿产资源开发利用提供科学的数据支撑。孙建华[8]、袁新悦等[9]论证了无人机在小范围大比例尺地形测量及地貌形态调查中的可行性。张艳博等[10]、宿文松[11]、汤昭辉等[12]利用无人机进行三维建模，并对模型精度进行了分析，结果表明，模型精度较好。刘心知[13]、金爱兵等[14]、安世缘[15]、成建华[16]利用无人机采集复杂工况下的矿山边坡数据，快速建立三维数值模型，可满足边坡稳定性分析需求。闻彩焕等[17]、张鸽等[18]利用无人机对矿区进行实景三维建模，并对露天矿山生态修复进行全过程管理。以上众多学者针对无人机矿山测绘及应用开展了大量研究，并取得了丰硕成果。
然而，上述研究多采用一种无人机、一种航高或一种像控点布设方案进行数据采集或数据处理及分析，未考虑不同无人机机型、不同航高和不同像控点布设方案对点位及模型精度的影响，也未考虑无人机矿山测量内、外业工作效率的问题。鉴于此，本文选用2种机型无人机，分别对矿区进行4种不同航高的外业数据采集，内业数据处理中各工况分别选取4种像控点布设方案，分析不同航高、不同像控点布设方案对点位精度的影响，同时考虑各工况下外业及内业耗时，并对无人机矿山测量效率进行分析，以期找出无人机矿山测绘精度、效率及成本的平衡，为行业工作者提供参考。
1　倾斜摄影测量及三维建模
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倾斜摄影测量技术是指在传统摄影测量的基础上，通过配置多台传感器或调整传感器视角以获得更完整、更具体的地物地貌数据的技术。目前无人机主要通过两种方式获取多视角影像，第一种是无人机挂载多镜头传感器，飞行一架次可同时获取5个视角的数据；第二种是无人机挂载单镜头，通过增加航线和智能摆拍可获取6个视角的数据。
在获得倾斜影像后，基于多视立体视觉算法（PMVS）和运动结构恢复算法（SfM），通过大疆智图或Context Capture等建模软件生成三维模型，经过多视角影像几何校正、联合平差处理等步骤，可生成高密度三维点云数据，同时提取地面纹理、高程、颜色等信息，生成高精度的三维模型，能直接反映区域目标内的地形、地貌及建筑物细节特征，生产周期短，成本低。
2　研究区概况及技术路线
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2.1　研究区概况
研究区位于湖南省湘乡市境内的湘潭钢铁集团有限公司湘乡白云石矿，矿山用地隶属湘乡市棋梓镇连云村。矿山生产规模为180万t/a，矿区面积0.6174 km2，开采深度+256~+80 m，矿区北部为小万罗山采区，南部为豹子山采区。北部小万罗山采区边坡地形落差较大，最高约+232 m，最低约+120 m。矿区东西长1 000 m，南北长800 m，属湘中地区典型的大高差非煤露天矿山。
2.2　技术路线
为研究不同机型、不同航高、不同像控点布设方案对最终测绘成果精度的影响，首先对无人机进行选型，参考目前无人机测绘作业主流设备，选择大疆经纬M300 RTK和Mavic 3E两种机型的无人机进行作业，无人机参数见表1。其次分别选取仿地飞行100 m、定高飞行100 m、定高飞行150 m和定高飞行200 m等4种不同航高进行数据采集，并在数据后处理阶段，选取0（免像控）、3个、5个、7个像控点作为已知点进行刺点，进而对无人机数据精度开展定性与定量分析。与此同时，统计不同工况下像控点布设、外业数据采集及内业数据处理耗时，并对不同工况下无人机作业效率进行分析。技术路线如图1所示。
3　无人机航线规划与数据采集
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3.1　航线规划
矿区开采台阶基本呈东西向展布，为更好地体现台阶立面细节，本次采用“之”字形航线方案，航线布设为东北西南方向，航向重叠度为80％，旁向重叠度为70％。为保证矿区模型的完整性，航线沿矿界外扩50 m，飞行速度设定为12 m/s，航线规划如图2所示。
3.2　数据采集
M300 RTK搭载成都睿铂DG3M五镜头半画幅相机，总像素约1.5亿；Mavic 3E采用自带的双镜头，其中广角镜头为4/3英寸COMS，有效像素2 000万，长焦镜头为1/2英寸COMS，有效像素1 200万，该机型采用智能摆拍模式。两种机型均集成有实时动态载波相位差分技术（RTK）模块，定位精度可达厘米级。
使用大疆DJI Pilot 2软件进行航线规划，由于矿区范围较大，为保证遥控器与无人机的通信，选择矿山+220 m处的平台作为起飞点，两种机型各自执行仿地飞行100 m、定高飞行100 m、定高飞行150 m和定高飞行200 m的航高数据采集。在执行仿地飞行作业时，M300 RTK将前期已在地理空间数据云平台下载好的矿区30 m精度的DEM数据作为底图，导入到遥控器中，从而实现仿地飞行，Mavic 3E具有实时仿地飞行功能，可直接作业。
本研究在矿山周边及矿区内部平台共布设10个像控点，像控点布设如图3所示。采用GNSS-RTK对像控点进行测量，获取CGCS2000坐标，同时在湖南CORS系统中求得各点的高程异常值，将测得的大地高修正为正常高，获得像控点的三维坐标，结果见表2。在获取坐标后，将其与三维模型上各点位进行对比，进而分析三维建模的精度。
3.3　数据处理
数据均采用大疆智图（V4.2.0版）进行处理，主要流程为新建可见光任务、导入影像数据、空三加密、点云重建、三维建模，最终获得研究区的三维模型及三维点云数据。
4　无人机测绘精度分析
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4.1　三维模型完整性分析
针对不同机型、不同航高下获取的露天矿山三维模型，分别从整体完整性和局部细节纹理两方面对模型进行对比研究。不同工况下，矿山整体三维模型如图4所示。由图4可知，不同工况下所获取的矿山三维模型均较为完整，未出现空洞等情况。就地面采样分辨率（GSD）而言，M300 RTK仿地飞行100 m精度最高，矿区各区域均可达1.51 cm，Mavic 3E定高飞行200 m在矿山底部最低精度为7.6 cm。整体来看，仿地飞行100 m的细节纹理效果最佳。
矿山局部三维模型如图5所示。由图5可知，同一机型随着航高的增加，地面分辨率逐渐降低，矿山牌匾逐步模糊。Mavic 3E仅在仿地飞行100 m工况下能够清晰辨识矿山牌匾，其他工况均已模糊，而M300 RTK直到定高飞行200 m工况下图像才变模糊，前3个工况可明显辨识矿山牌匾，且仿地飞行100 m清晰度最高，细节最好，变形最小。
4.2　三维模型精度分析
后期数据处理中，除选取用作刺点的像控点外，其余各像控点均作为检查点，与前期GNSS-RTK测量值进行对比，从而评估矿山三维模型精度。各工况参与解算的像控点见表3。
基于矿山三维模型，采用GNSS-RTK实测的像控点坐标作为相对真值，与模型中检查点的数据对比求差，通过计算中误差，对三维模型的精度进行评价。参考《低空数字航空摄影测量内业规范》 （CH/T 2003—2021），中误差的计算公式为：
式中：Δxi、Δyi为检查点的平面坐标较差；Δhi为检查点的高程较差；n为观测次数；ms为检查点平面中误差，mh为检查点高程中误差；m3D为检查点三维中误差。
各工况检查点的平面中误差和模型中误差见表4。图6为模型三维中误差柱状图。
由表4及图6可知，M300 RTK在仿地飞行100 m工况下，像控点数量为7个时，精度最高，平面精度为±3.2 cm，三维精度为±4.0 cm；Mavic 3E在定高飞行200 m工况及免像控条件下，精度最低，平面精度为±13.9 cm，三维精度为±14.8 cm。同等工况下，Mavic 3E整体精度低于M300 RTK，同一机型时，参与解算的像控点数量越多，三维模型整体平面中误差越小，精度越高，像控点数量相同时，仿地飞行精度高于定高飞行，随着飞行高度的增加，精度逐渐下降。参考《工程测量通用规范》（GB 55018—2021），本项目的测量成果均可满足1∶1 000比例尺的测图要求。
5　无人机测绘效率分析
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在测区内共布设10个像控点，根据现场计时，共耗时2 h，平均每个像控点耗时12 min；内、外业耗时统计见表5。
通过分析表5可知，随着航高的增加，总耗时呈下降趋势，同一航高工况下，总耗时仅与外业像控点的数量有关；Mavic 3E的内业耗时远小于M300 RTK，前者的内业耗时约占后者的3.8％~8.8％；Mavic 3E外业耗时约占总耗时的27.5％~47.1％，M300 RTK外业耗时约占总耗时的3.1％~5.1％；在满足精度要求的前提下，Mavic 3E的作业效率明显高于M300 RTK，尤其是Mavic 3E免像控配合实时仿地飞行模式。
6　结论
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本研究采用两种机型无人机，针对矿区开展4种不同航高的外业数据采集，在内业数据处理中，分析4种不同像控点布设方案对点位精度的影响，同时统计不同工况下外业及内业的耗时情况，进而对无人机矿山测量效率进行分析，得出的主要结论如下。
（1）M300 RTK的整体精度上高于同工况Mavic 3E，仿地飞行100 m模式下，测量成果可满足1∶1 000大比例尺地形图的精度要求；但随着航高的增加，两种机型无人机的精度不断降低。在航高相同的情况下，随着地面像控点数量的增加，两种机型无人机的精度不断提高，但提高的幅度不大。
（2）在相同航高工况下，两种机型无人机的外业耗时基本一致，内业耗时差别较大，Mavic 3E的内业耗时均远小于M300 RTK，前者的内业耗时约占后者的3.8％~8.8％。
（3）在大高差露天矿山这种地形急剧变化的情况下，采用Mavic 3E实时仿地飞行配合免像控模式，既可免除外业布设像控点的作业步骤，又使得内业耗时大幅减少，在保证精度的前提下，可大幅提高矿山无人机测绘作业效率。
（4）露天矿山非开采区因植被覆盖导致Mavic 3E携带的可见光传感器无法穿透至地表，在地形测量中存在一定误差，而M300 RTK可以换装激光雷达，可直接获取植被区的点云数据，精度较高。后续研究应进一步推进激光雷达的小型化、低成本化和低功耗化，使其与轻小型无人机实现适配，将极大扩展无人机在矿山领域的应用范围，为后期智慧矿山建设提供数据支撑。
基金
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  • 湖南省教育厅科学研究项目(24C0950)
  • 湘潭市科技创新项目(CG-ZDJH20241030)
  • 湖南理工职业技术学院自科重点课题(2024HNVITZK003)
文献
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2025年第45卷第12期
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  • 接收时间：2025-01-08
  • 首发时间：2026-10-28
  • 出版时间：2025-12-25
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  • 收稿日期：2025-01-08
基金
湖南省教育厅科学研究项目(24C0950)
湘潭市科技创新项目(CG-ZDJH20241030)
湖南理工职业技术学院自科重点课题(2024HNVITZK003)
作者信息
    1.湖南理工职业技术学院，湖南 湘潭市　411104
    2.长沙市光启勘测工程技术有限公司，湖南 长沙　410114
    3.湘潭钢铁集团有限公司湘乡白云石矿，湖南 湘潭市　411423
    4.广东瑞东勘察基础工程有限公司，广东 东莞市　523000
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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