Abstract
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.
Recommended Citation
E. Essien and S. Frimpong, "Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends," Drones, vol. 9, no. 6, article no. 433, MDPI, Jun 2025.
The definitive version is available at https://doi.org/10.3390/drones9060433
Department(s)
Mining Engineering
Publication Status
Open Access
Keywords and Phrases
3D object detection; autonomous trucks; deep learning; sensor fusion; underground mines; YOLO algorithms
International Standard Serial Number (ISSN)
2504-446X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jun 2025
Comments
Centers for Disease Control and Prevention, Grant CDC-NIOSH-U60OH012685-01-00