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AutoMine: A Multimodal Dataset for Robot Navigation in Open‐Pit Mines.

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Title: AutoMine: A Multimodal Dataset for Robot Navigation in Open‐Pit Mines.
Authors: Li, Yuchen1,2,3,4 (AUTHOR), Teng, Siyu1,2,3 (AUTHOR), Wang, Junhui5 (AUTHOR), Ai, Yunfeng1,6 (AUTHOR), Tian, Bin1,7 (AUTHOR), Xuanyuan, Zhe2 (AUTHOR) zhexuanyuan@uic.edu.cn, Bing, Zhenshan4 (AUTHOR), Knoll, Alois C.4 (AUTHOR), Wang, Fei‐Yue7 (AUTHOR), Chen, Long1,7,8,9 (AUTHOR) long.chen@ia.ac.cn
Source: Journal of Field Robotics. Jun2025, Vol. 42 Issue 4, p1523-1536. 14p.
Subjects: Autonomous vehicles, Computing platforms, Data analysis, Acquisition of data, Robotics
Abstract: In the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open‐pit mine, a typical scenario within closed‐field environments, has garnered limited attention in autonomous driving, primarily owing to the scarcity of data and experimental benchmarks. This work presents original data collected from five platforms, comprising one passenger vehicle, three wide‐body trucks, and one mining truck, across eight different mining sites. We provide a comprehensive elucidation of platform types, sensors, calibration methodologies, synchronization techniques, data collection approaches, and a thorough analysis of the data characteristics. In addition, we offer a detailed benchmark comparison of short and long odometry and navigation performance across multiple vehicles in open‐pit mines. With comprehensive data characteristics, experimental performance evaluations, and thorough analysis, we believe that this work establishes a robust research foundation for navigation and fusion methods in open‐pit mines, thereby constituting a significant contribution to the autonomous driving and field robotics communities. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: AutoMine: A Multimodal Dataset for Robot Navigation in Open‐Pit Mines.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Yuchen%22">Li, Yuchen</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Teng%2C+Siyu%22">Teng, Siyu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Junhui%22">Wang, Junhui</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ai%2C+Yunfeng%22">Ai, Yunfeng</searchLink><relatesTo>1,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Bin%22">Tian, Bin</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xuanyuan%2C+Zhe%22">Xuanyuan, Zhe</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhexuanyuan@uic.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bing%2C+Zhenshan%22">Bing, Zhenshan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Knoll%2C+Alois+C%2E%22">Knoll, Alois C.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Fei‐Yue%22">Wang, Fei‐Yue</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Long%22">Chen, Long</searchLink><relatesTo>1,7,8,9</relatesTo> (AUTHOR)<i> long.chen@ia.ac.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Field+Robotics%22">Journal of Field Robotics</searchLink>. Jun2025, Vol. 42 Issue 4, p1523-1536. 14p.
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  Data: In the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open‐pit mine, a typical scenario within closed‐field environments, has garnered limited attention in autonomous driving, primarily owing to the scarcity of data and experimental benchmarks. This work presents original data collected from five platforms, comprising one passenger vehicle, three wide‐body trucks, and one mining truck, across eight different mining sites. We provide a comprehensive elucidation of platform types, sensors, calibration methodologies, synchronization techniques, data collection approaches, and a thorough analysis of the data characteristics. In addition, we offer a detailed benchmark comparison of short and long odometry and navigation performance across multiple vehicles in open‐pit mines. With comprehensive data characteristics, experimental performance evaluations, and thorough analysis, we believe that this work establishes a robust research foundation for navigation and fusion methods in open‐pit mines, thereby constituting a significant contribution to the autonomous driving and field robotics communities. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/rob.22469
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        Text: English
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        PageCount: 14
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    Subjects:
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Computing platforms
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Robotics
        Type: general
    Titles:
      – TitleFull: AutoMine: A Multimodal Dataset for Robot Navigation in Open‐Pit Mines.
        Type: main
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            NameFull: Li, Yuchen
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            NameFull: Wang, Junhui
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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