mlsanityData to 14 Oct 2026

A review of laser scanning for geological and geotechnical applications in underground mining

arXiv 2211.11181 ↗First posted 21 Nov 2022cs.CVcited by 4

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Abstract (arXiv)

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 186 references that are in the map. First posted on arXiv 21 Nov 2022.

Builds on

8 of 186 references.

References · 8

  1. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  2. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
  3. 2019DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registrationcited by 77
  4. 2018Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networkscited by 44
  5. 2020A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligencecited by 35
  6. 2018Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mappingcited by 23
  7. 2017A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloudcited by 5
  8. 2021Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environmentcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.