Closure monitoring in Potash Mines using LiDAR

Errington, Angus F. C.; Daku, B.L.F.; Prugger, Arnfinn

doi:10.1109/iecon.2010.5675071

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…where (12,18) implies that the transition from the first linear function to the second occurs in this region of r between 12 and 18. Equation (4) can be rewritten with unknown parameters f (r) = a 1 r + a 2 r < (12, 18) a 3 r + a 4 r ≥ (12, 18) ,…”

Section: Normalization Methodsmentioning

confidence: 97%

“…18 Many scans were taken to produce a satisfactory model of the mine room (tunnel) as the resolution of the scanner is quite low. A large number of scans means that the acquisition takes more time but it is advantageous in studies like this as there are many overlapping scans.…”

Section: Methodsmentioning

confidence: 99%

“…(r) is a piecewise continuous linear function of the form f (r) = −0.0038r + 0.145 r <(12,18) 0.0007r + 0.071 r ≥(12,18) …”

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confidence: 99%

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A model based approach to intensity normalization for terrestrial laser scanners

2011

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Terrestrial Laser Scanners (TLS) measure distances and record angles to objects in the surrounding environment. In addition, a TLS also measures the amount of reflected light (intensity). The intensity value provided by the TLS is a function of the distance to the object, angle of incidence, the object's reflectance properties and internal parameters of the laser scanner. In some cases it is possible to obtain the reflectance properties of an object from the intensity value, distance and angle of incidence. The object reflectance provides information that can be used to improve the speed and accuracy in segmentation or registration algorithms. In order to model the relationship between intensity and reflectance it is necessary to calibrate the model for the specific laser scanner using reflectance targets. These targets have surfaces calibrated to a specific reflectance value. The targets are expensive and it is a time consuming task to calibrate the model in this manner. Alternatively, this paper presents a method to determine the 'normalized intensity' using a similar model but without using the calibrated reflectance targets. Instead, overlapping registered scans are used to determine the model parameters. The normalized intensity can be used in the same way that object reflectance is used in segmentation and registration algorithms. The only difference is that it is not a true reflectance value.The scans used to test the method were taken with a 3DLS-K2 TLS. It is shown that using this method produces normalized intensity values that are independent of distance, instead providing a value related to the actual object's reflectance.

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Section: Normalization Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

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A model based approach to intensity normalization for terrestrial laser scanners

2011

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“…In recent years, LiDAR, a technique used in laser scanning for 3D mapping, has been widely used for underground 3D imaging [36]- [38]. In underground environment, laser scanning has normally been used to monitor tunnel closures [9], deformations [10], [14], [40], and cavity collapse [8]. With a large scanning range and being independent of lighting condition [13], LiDAR offers new opportunities for underground ground support monitoring, in particular, rock bolt identification.…”

Section: Introductionmentioning

confidence: 99%

A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds

Saydam¹,

Liu²,

Li³

et al. 2021

IEEE Access

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Rock bolts have been widely used to enhance the structural stability of underground infrastructures. Careful tracking of rock bolt positions is highly significant since it assists with operational success of ground support and has applications to predictive maintenance practices. This paper presents an effective algorithm, CFBolt, to detect rock bolts from a 3D laser scanned point cloud. Considering that rock bolts are relatively tiny objects, CFBolt follows a two-step coarse-to-fine strategy. It first computes a single-scale proportion of variance (POV) for each point as the local point descriptor and filters out near 95% not-bolt points with a simple but effective classifier, Linear Discriminant Analysis (LDA), which allows for the pruned point cloud to be then used as a compatible input to a deep neural network, designed and trained to precisely detect rock bolts from the pruned point cloud. CFBolt was tested for detecting rock bolts from LiDAR scan data collected from Sydney's civil tunnelling project site. The entire dataset contains more than 160 million points. The obtained scores of Intersection over Union (IoU) and precision for individual bolt points were 89.33% and 92.04%, respectively. For rock bolt objects, the precision and recall were 98.34% and 98.73%, respectively. The detection quality of CFBolt is superior to the state-of-the-art 3D object detection algorithms and the newest rock bolt detection algorithm, demonstrating the robustness and effectiveness of CFBolt.

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