Academic Journal
Stereo Vision-Based Underground Muck Pile Detection for Autonomous LHD Bucket Loading.
| Title: | Stereo Vision-Based Underground Muck Pile Detection for Autonomous LHD Bucket Loading. |
|---|---|
| Authors: | Hennen, Emilia, Pekarski, Adam, Storoschewich, Violetta, Clausen, Elisabeth |
| Source: | Sensors (14248220); Sep2025, Vol. 25 Issue 17, p5241, 18p |
| Subject Terms: | Stereo vision (Computer science), Loaders (Machines), Mines & mineral resources, Spatial data structures, Multisensor data fusion, Automated materials handling, Computational topology |
| Abstract: | To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Stereo Vision-Based Underground Muck Pile Detection for Autonomous LHD Bucket Loading. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hennen%2C+Emilia%22">Hennen, Emilia</searchLink><br /><searchLink fieldCode="AR" term="%22Pekarski%2C+Adam%22">Pekarski, Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Storoschewich%2C+Violetta%22">Storoschewich, Violetta</searchLink><br /><searchLink fieldCode="AR" term="%22Clausen%2C+Elisabeth%22">Clausen, Elisabeth</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); Sep2025, Vol. 25 Issue 17, p5241, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Stereo+vision+%28Computer+science%29%22">Stereo vision (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Loaders+%28Machines%29%22">Loaders (Machines)</searchLink><br /><searchLink fieldCode="DE" term="%22Mines+%26+mineral+resources%22">Mines & mineral resources</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+data+structures%22">Spatial data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Automated+materials+handling%22">Automated materials handling</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+topology%22">Computational topology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s25175241 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 5241 Subjects: – SubjectFull: Stereo vision (Computer science) Type: general – SubjectFull: Loaders (Machines) Type: general – SubjectFull: Mines & mineral resources Type: general – SubjectFull: Spatial data structures Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Automated materials handling Type: general – SubjectFull: Computational topology Type: general Titles: – TitleFull: Stereo Vision-Based Underground Muck Pile Detection for Autonomous LHD Bucket Loading. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hennen, Emilia – PersonEntity: Name: NameFull: Pekarski, Adam – PersonEntity: Name: NameFull: Storoschewich, Violetta – PersonEntity: Name: NameFull: Clausen, Elisabeth IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 25 – Type: issue Value: 17 Titles: – TitleFull: Sensors (14248220) Type: main |
| ResultId | 1 |