Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement | IEEE Journals & Magazine | IEEE Xplore

Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement


Abstract:

Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern se...Show More

Abstract:

Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern sensing technology, these measurements can be taken on 3-D point clouds and 2-D optical images that provide a high level of structural accuracy and visual detail. Doing so allows engineers to obtain the needed data with relative ease while eliminating the biases and hazards inherent in taking manual measurements. This letter presents an approach for fusing the 2-D and 3-D data in natural and unstructured scenes. This includes a novel method for visualizing imagery obtained with very different sensors to maximize their visual similarity making registration a more tangible task. To show the effectiveness of our registration methodology, we evaluate measurements taken manually and digitally on rock facet and cut discontinuity orientations in Rolla, MO. Our method is able to align the 2-D and 3-D data with an accuracy of under 2 cm. The median difference between measurements manually obtained by a geological engineer and those obtained with our proposed software is 3.65.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 15, Issue: 6, June 2018)
Page(s): 947 - 951
Date of Publication: 27 April 2018

ISSN Information:

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References is not available for this document.

I. Introduction

In mountainous and hilly regions, roadways commonly pass alongside tall walls of rocks. This poses many potential obstacles and dangers for drivers, road workers, and engineers who travel through these areas or who are responsible for building and maintaining the road infrastructure [1]. Discontinuities in the rocks, oftentimes, cause rock mass to break off along existing planar discontinuities that occur either naturally or as a result of engineered rock cutting during the road construction process [2]. Using analytical tools, the arrangement and orientations of single discontinuities or groups of discontinuities can actually be used to study rock stability and predict detachment events [2]. However, obtaining the measurements manually tends to be slow and cumbersome, and in some cases, dangerous because of potentially falling rock [3]. Due to time constraints and safety concerns, they are often only able to be employed in easily accessible locations, such as the base of a slope [4]. These types of restrictions can cause sampling biases and inaccuracies [5]. However, modern sensing technologies, such as photographs and light detection and ranging (LIDAR) laser scans, can be used to capture data more quickly and safely than traditional techniques [6]–[8].

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1.
M. Lato, J. Hutchinson, M. Diederichs, D. Ball and R. Harrap, "Engineering monitoring of rockfall hazards along transportation corridors: Using mobile terrestrial LiDAR", Natural Hazards Earth Syst. Sci., vol. 9, no. 3, pp. 935-946, 2009.
2.
J. Kemeny and R. Post, "Estimating three-dimensional rock discontinuity orientation from digital images of fracture traces", Comput. Geosci., vol. 29, no. 1, pp. 65-77, 2003.
3.
M. J. Lato and M. Vöge, "Automated mapping of rock discontinuities in 3D Lidar and photogrammetry models", Int. J. Rock Mech. Mining Sci., vol. 54, pp. 150-158, Sep. 2012.
4.
A. Strouth and E. Eberhardt, "The use of LiDAR to overcome rock slope hazard data collection challenges at Afternoon Creek Washington", Proc. Methods Rock Face Characterization Workshop U.S. Symp. Rock Mech., pp. 49-62, Jun. 2006.
5.
M. J. Lato, M. S. Diederichs and D. J. Hutchinson, "Bias correction for view-limited Lidar scanning of rock outcrops for structural characterization", Rock Mech. Rock Eng., vol. 43, no. 5, pp. 615-628, 2010.
6.
M. I. Olariu, J. F. Ferguson, C. L. V. Aiken and X. Xu, "Outcrop fracture characterization using terrestrial laser scanners: Deep-water Jackfork sandstone at Big Rock Quarry Arkansas", Geosphere, vol. 4, no. 1, pp. 247-259, 2008.
7.
J. N. Otoo, N. H. Maerz, L. Xiaoling and Y. Duan, "3-D discontinuity orientations using combined optical imaging and LiDAR techniques", Proc. 45th U.S. Rock Mech. Symp., pp. 1-9, 2011.
8.
K. Khoshelham, D. Altundag, D. Ngan-Tillard and M. Menenti, "Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning", Int. J. Rock Mech. Mining Sci., vol. 48, no. 8, pp. 1215-1223, 2011.
9.
R. Post, "Characterizing of joints and fractures in a rock mass using digital image processing", pp. 1-105, 2001.
10.
W. C. Haneberg, "Book and software ReviewsSirovision", Environ. Eng. Geosci., vol. 12, no. 3, pp. 283-285, 2006.
11.
W. C. Haneberg, "Using close range terrestrial digital photogrammetry for 3-D rock slope modeling and discontinuity mapping in the United States", Bull. Eng. Geol. Environ., vol. 67, no. 4, pp. 457-469, 2008.
12.
J. L. Schönberger and J. Frahm, "Structure-from-motion revisited", Proc. IEEE Comput. Vis. Pattern Recognit., pp. 4104-4113, Jun. 2016.
13.
M. Sturzenegger and D. Stead, "Close-range terrestrial digital photogrammetry and terrestrial laser scanning for discontinuity characterization on rock cuts", Eng. Geol., vol. 106, no. 3, pp. 163-182, 2009.
14.
G. Gigli and N. Casagli, "Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds", Int. J. Rock Mech. Mining Sci., vol. 48, no. 2, pp. 187-198, 2011.
15.
D. García-Sellés, O. Falivene, P. Arbués, O. Gratacos, S. Tavani and J. A. Muñoz, "Supervised identification and reconstruction of near-planar geological surfaces from terrestrial laser scanning", Comput. Geosci., vol. 37, no. 10, pp. 1584-1594, 2011.
16.
A. Riquelme, A. Abellán, R. Tomás and M. Jaboyedoff, "A new approach for semi-automatic rock mass joints recognition from 3D point clouds", Comput. Geosci., vol. 68, pp. 38-52, Jul. 2014.
17.
J. N. Otoo, N. H. Maerz, X. Li and Y. Duan, "Verification of a 3-D LiDAR viewer for discontinuity orientations", Rock Mech. Rock Eng., vol. 46, no. 3, pp. 543-554, 2013.
18.
R. Schnabel, R. Wahl and R. Klein, "Efficient ransac for point-cloud shape detection", Comput. Graph. Forum, vol. 26, no. 2, pp. 214-226, 2007.
19.
N. Brodu and D. Lague, "3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology", ISPRS J. Photogram. Remote Sens., vol. 68, pp. 121-134, Mar. 2012.
20.
Y. Li, Q. Zheng, A. Sharf, D. Cohen-Or, B. Chen and N. Mitra, "2D-3D fusion for layer decomposition of urban facades", Proc. Int. Conf. Comput. Vis., pp. 882-889, 2011.
21.
I. Stamos, L. Liu, C. Chen, G. Wolberg, G. Yu and S. Zokai, "Integrating automated range registration with multiview geometry for the photorealistic modeling of large-scale scenes", Int. J. Comput. Vis., vol. 78, no. 2, pp. 237-260, 2008.
22.
G. Pandey, J. R. McBride, S. Savarese and R. M. Eustice, "Automatic targetless extrinsic calibration of a 3D Lidar and camera by maximizing mutual information", Proc. AAAI Nat. Conf. Artif. Intell., pp. 2053-2059, 2012.
23.
B. C. Matei, N. V. Valk, Z. Zhu, H. Cheng and H. S. Sawhney, "Image to LIDAR matching for geotagging in urban environments", Proc. Appl. Comput. Vis., pp. 413-420, Jan. 2013.
24.
L. Yu, K. Efstathiou, P. Isenberg and T. Isenberg, "CAST: Effective and efficient user interaction for context-aware selection in 3D particle clouds", IEEE Trans. Vis. Comput. Graphics, vol. 22, no. 1, pp. 886-895, Jan. 2016.
25.
F. Bacim, M. Nabiyouni and D. A. Bowman, "Slice-n-swipe: A free-hand gesture user interface for 3D point cloud annotation", Proc. Symp. 3D User Interfaces, pp. 185-186, 2014.
26.
A. Irschara, C. Zach, J.-M. Frahm and H. Bischof, "From structure-from-motion point clouds to fast location recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2599-2606, Jun. 2009.
27.
J. Canny, "A computational approach to edge detection", IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679-698, Nov. 1986.
28.
J. Shan and C. K. Toth, Topographic Laser Ranging and Scanning: Principles and Processing, Boca Raton, FL, USA:CRC Press, 2008.
29.
D. Carrea, A. Abellan, F. Humair, B. Matasci, M.-H. Derron and M. Jaboyedoff, "Correction of terrestrial LiDAR intensity channel using Oren–Nayar reflectance model: An application to lithological differentiation", ISPRS J. Photogram. Remote Sens., vol. 113, pp. 17-29, Mar. 2016.
30.
R. G. von Gioi, J. Jakubowicz, J.-M. Morel and G. Randall, "LSD: A fast line segment detector with a false detection control", IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 4, pp. 722-732, Apr. 2010.

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