Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends
Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility...
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MDPI AG
2025-06-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/6/433 |
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| author | Ellen Essien Samuel Frimpong |
| author_facet | Ellen Essien Samuel Frimpong |
| author_sort | Ellen Essien |
| collection | DOAJ |
| description | Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks. |
| format | Article |
| id | doaj-art-7eed20b7555f44e6a933c51ff83a12ee |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-7eed20b7555f44e6a933c51ff83a12ee2025-08-20T03:27:18ZengMDPI AGDrones2504-446X2025-06-019643310.3390/drones9060433Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future TrendsEllen Essien0Samuel Frimpong1Department of Mining and Explosives Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Mining and Explosives Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAIntegrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks.https://www.mdpi.com/2504-446X/9/6/4333D object detectionautonomous trucksdeep learningsensor fusionunderground minesYOLO algorithms |
| spellingShingle | Ellen Essien Samuel Frimpong Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends Drones 3D object detection autonomous trucks deep learning sensor fusion underground mines YOLO algorithms |
| title | Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends |
| title_full | Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends |
| title_fullStr | Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends |
| title_full_unstemmed | Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends |
| title_short | Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends |
| title_sort | enhancing autonomous truck navigation in underground mines a review of 3d object detection systems challenges and future trends |
| topic | 3D object detection autonomous trucks deep learning sensor fusion underground mines YOLO algorithms |
| url | https://www.mdpi.com/2504-446X/9/6/433 |
| work_keys_str_mv | AT ellenessien enhancingautonomoustrucknavigationinundergroundminesareviewof3dobjectdetectionsystemschallengesandfuturetrends AT samuelfrimpong enhancingautonomoustrucknavigationinundergroundminesareviewof3dobjectdetectionsystemschallengesandfuturetrends |