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Range Sensing

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Springer Handbook of Robotics

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Abstract

Range sensors are devices that capture the three-dimensional (GlossaryTerm

3-D

) structure of the world from the viewpoint of the sensor, usually measuring the depth to the nearest surfaces. These measurements could be at a single point, across a scanning plane, or a full image with depth measurements at every point. The benefits of this range data is that a robot can be relatively certain where the real world is, relative to the sensor, thus allowing the robot to more reliably find navigable routes, avoid obstacles, grasp objects, act on industrial parts, etc.

This chapter introduces the main representations for range data (point sets, triangulated surfaces, voxels), the main methods for extracting usable features from the range data (planes, lines, triangulated surfaces), the main sensors for acquiring it (Sect. 31.1 – stereo and laser triangulation and ranging systems), how multiple observations of the scene, for example, as if from a moving robot, can be registered (Sect. 31.3) and several indoor and outdoor robot applications where range data greatly simplifies the task (Sect. 31.4).

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Abbreviations

1-D:

one-dimensional

2-D:

two-dimensional

2.5-D:

two-and-a-half-dimensional

3-D:

three-dimensional

6-D:

six-dimensional

ASIC:

application-specific feature transform

DARPA:

Defense Advanced Research Projects Agency

DOF:

degree of freedom

DSP:

digital signal processor

EM:

expectation maximization

FMCW:

frequency modulation continuous wave

FOV:

field of view

FPGA:

field-programmable gate array

GPS:

global positioning system

GPU:

graphics processing unit

ICP:

iterative closest point

IMU:

inertial measurement unit

IR:

infrared

LADAR:

laser radar

LED:

light-emitting diode

LIDAR:

light detection and ranging

LMS:

laser measurement system

LOG:

Laplacian of Gaussian

MLS:

multilevel surface map

PCA:

principal component analysis

PC:

personal computer

PFH:

point feature histogram

RANSAC:

random sample consensus

SFM:

structure from motion

SIFT:

scale-invariant feature transform

SLAM:

simultaneous localization and mapping

SNR:

signal-to-noise ratio

SVD:

singular value decomposition

TOF:

time-of-flight

References

  1. H. Houshiar, J. Elseberg, D. Borrmann, A. Nüchter: A study of projections for key point based registration of panoramic terrestrial 3D laser scans, J.Geo-Spat. Inf. Sci. 18(1), 11–31 (2015)

    Article  Google Scholar 

  2. Velodyne: High definition lidar, http://velodynelidar.com/ (2015)

  3. R. Stettner, H. Bailey, S. Silverman: Three-dimensional flash Ladar focal planes and time-dependent imaging, Int. J. High Speed Electron. Syst. 18(2), 401–406 (2008)

    Article  Google Scholar 

  4. S.B. Gokturk, H. Yalcin, C. Bamji: A time-of-flight depth sensor – system description, issues and solutions, Computer Vis.Pattern Recognit. Workshop (CVPRW) (2004)

    Google Scholar 

  5. T. Oggier, M. Lehmann, R. Kaufmannn, M. Schweizer, M. Richter, P. Metzler, G. Lang, F. Lustenberger, N. Blanc: An all-solid-state optical range camera for 3D-real-time imaging with sub-centimeter depth-resolution (SwissRanger), Proc. SPIE 5249, 534–545 (2003)

    Article  Google Scholar 

  6. U. Wong, A. Morris, C. Lea, J. Lee, C. Whittaker, B. Garney, R. Whittaker: Red: Comparative evaluation of range sensing technologies for underground void modeling, Proc. IEEE/RSJ Int. Conf.Intell. RobotsSyst. (IROS) (2011) pp. 3816–3823

    Google Scholar 

  7. D.D. Lichti: A review of geometric models and self-calibration methods for terrestrial laser scanner, Bol. Cienc. Géod. 16(1), 3–19 (2010)

    Google Scholar 

  8. G. Iddan, G. Yahav: 3D imaging in the studio (and elsewhere…), Proc.SPIE 4298 (2003) pp. 48–55

    Google Scholar 

  9. TriDiCam GmbH: http://www.tridicam.de/en.html (2015)

  10. R. Hartley, A. Zisserman: Multiple View Geometry in Computer Vision (Cambridge Univ. Press, Cambridge 2000)

    MATH  Google Scholar 

  11. S. Barnard, M. Fischler: Computational stereo, ACM Comput. Surv. 14(4), 553–572 (1982)

    Article  Google Scholar 

  12. D. Scharstein, R. Szeliski, R. Zabih: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Int. J.Computer Vis. 47(1–3), 7–42 (2002)

    Article  MATH  Google Scholar 

  13. D. Scharstein, R. Szeliski: Middlebury College Stereo Vision Research Page, http://vision.middlebury.edu/stereo (2007)

  14. R. Zabih, J. Woodfill: Non-parametric local transforms for computing visual correspondence, Proc. Eur. Conf.Comput. Vis., Vol. 2 (1994) pp. 151–158

    Google Scholar 

  15. O. Faugeras, B. Hotz, H. Mathieu, T. Viéville, Z. Zhang, P. Fua, E. Théron, L. Moll, G. Berry, J. Vuillemin, P. Bertin, C. Proy: Real time correlation based stereo: algorithm implementations and applications, Int. J.Computer Vis. 47(1--3), 229–246 (2002)

    Google Scholar 

  16. M. Okutomi, T. Kanade: A multiple-baseline stereo, IEEE Trans. Pattern Anal. Mach. Intell. 15(4), 353–363 (1993)

    Article  Google Scholar 

  17. L. Matthies: Stereo vision for planetary rovers: stochastic modeling to near realtime implementation, Int. J. Comput. Vis 8(1), 71–91 (1993)

    Article  MathSciNet  Google Scholar 

  18. R. Bolles, J. Woodfill: Spatiotemporal consistency checking of passive range data, Proc. Int. Symp.Robotics Res. (1993)

    Google Scholar 

  19. P. Fua: A parallel stereo algorithm that produces dense depth maps and preserves image features, Mach. Vis.Appl. 6(1), 35–49 (1993)

    Article  Google Scholar 

  20. H. Moravec: Visual mapping by a robot rover, Proc. Int. Jt. Conf.Artif. Intell. (IJCAI) (1979) pp. 598–600

    Google Scholar 

  21. A. Adan, F. Molina, L. Morena: Disordered patterns projection for 3D motion recovering, Proc. Int. Conf.3D Data Process. Vis. Transm. (2004) pp. 262–269

    Google Scholar 

  22. Videre Design LLC: http://www.videredesign.com (2007)

  23. Point Grey Research Inc.: http://www.ptgrey.com (2015)

  24. C. Zach, A. Klaus, M. Hadwiger, K. Karner: Accurate dense stereo reconstruction using graphics hardware, Proc. EUROGRAPHICS (2003) pp. 227–234

    Google Scholar 

  25. R. Yang, M. Pollefeys: Multi-resolution real-time stereo on commodity graphics hardware, Int. Conf. Comput. VisPattern Recognit., Vol. 1 (2003) pp. 211–217

    Google Scholar 

  26. K. Konolige: Small vision system. Hardware and implementation, Proc. Int. Symp. Robotics Res. (1997) pp. 111–116

    Google Scholar 

  27. Focus Robotics Inc.: http://www.focusrobotics.com (2015)

  28. TYZX Inc.: http://www.tyzx.com (2015)

  29. S.K. Nayar, Y. Nakagawa: Shape from Focus, IEEE Trans. Pattern Anal. Mach. Intell. 16(8), 824–831 (1994)

    Article  Google Scholar 

  30. M. Pollefeys, R. Koch, L. Van Gool: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters, Int. J.Computer Vis. 32(1), 7–25 (1999)

    Article  Google Scholar 

  31. A. Hertzmann, S.M. Seitz: Example-based photometric stereo: Shape reconstruction with general, Varying BRDFs, IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1254–1264 (2005)

    Article  Google Scholar 

  32. A. Lobay, D.A. Forsyth: Shape from texture without boundaries, Int. J. Comput. Vis. 67(1), 71–91 (2006)

    Article  Google Scholar 

  33. Wikipedia: http://en.wikipedia.org/wiki/List_of_fastest-selling_products (2015)

  34. K. Khoshelham, S.O. Elberink: Accuracy and resolution of kinect depth data for indoor mapping applications, Sensors 12(5), 1437–1454 (2012)

    Article  Google Scholar 

  35. P.J. Besl, N.D. McKay: A method for registration of 3D shapes, IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  36. Y. Chen, G. Medioni: Object modeling by registration of multiple range images, ImageVis. Comput. 10(3), 145–155 (1992)

    Google Scholar 

  37. Z. Zhang: Iterative Point Matching for Registration of Free–Form Curves, Tech. Rep. Ser., Vol. RR-1658 (INRIA–Sophia Antipolis, Valbonne Cedex 1992)

    Google Scholar 

  38. S. Rusinkiewicz, M. Levoy: Efficient variants of the ICP algorithm, Proc. 3rd Int. Conf.3D Digital ImagingModel. (2001) pp. 145–152

    Google Scholar 

  39. J.L. Bentley: Multidimensional binary search trees used for associative searching, Commun. ACM 18(9), 509–517 (1975)

    Article  MATH  Google Scholar 

  40. J.H. Friedman, J.L. Bentley, R.A. Finkel: An algorithm for finding best matches in logarithmic expected time, ACM Trans. on Math. Software 3(3), 209–226 (1977)

    Article  MATH  Google Scholar 

  41. M. Greenspan, M. Yurick: Approximate K-D tree search for efficient ICP, Proc. 4th IEEE Int. Conf. Recent Adv. 3D Digital ImagingModel. (2003) pp. 442–448

    Google Scholar 

  42. L. Hyafil, R.L. Rivest: Constructing optimal binary decision trees is NP-complete, Inf. Proc. Lett. 5, 15–17 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  43. N.J. Mitra, N. Gelfand, H. Pottmann, L. Guibas: Registration of point cloud data from a geometric optimization perspective, Proc. Eurographics/ACM SIGGRAPH Symp.Geom. Process. (2004) pp. 22–31

    Google Scholar 

  44. A. Nüchter, K. Lingemann, J. Hertzberg: Cached k-d tree search for ICP Algorithms, Proc. 6th IEEE Int. Conf.Recent Adv.3D Digital ImagingModel. (2007) pp. 419–426

    Google Scholar 

  45. K.S. Arun, T.S. Huang, S.D. Blostein: Least-squares fitting of two 3-D point sets, IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987)

    Article  Google Scholar 

  46. B.K.P. Horn, H.M. Hilden, S. Negahdaripour: Closed–form solution of absolute orientation using orthonormal matrices, J. Opt. Soc. Am. A 5(7), 1127–1135 (1988)

    Article  MathSciNet  Google Scholar 

  47. B.K.P. Horn: Closed–form solution of absolute orientation using unit quaternions, J. Opt. Soc. Am. A 4(4), 629–642 (1987)

    Article  Google Scholar 

  48. M.W. Walker, L. Shao, R.A. Volz: Estimating 3-d location parameters using dual number quaternions, J. Comput. Vis. Image Underst. 54, 358–367 (1991)

    MATH  Google Scholar 

  49. A. Nüchter, J. Elseberg, P. Schneider, D. Paulus: Study of parameterizations for the rigid body transformations of the scan registration problem, J. Comput. Vis. Image Underst. 114(8), 963–980 (2010)

    Article  Google Scholar 

  50. M.A. Fischler, R.C. Bolles: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Comm. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  51. N.J. Mitra, A. Nguyen: Estimating surface normals in noisy point cloud data, Proc. Symp. Comput. Geom. (SCG) (2003) pp. 322–328

    Google Scholar 

  52. H. Badino, D. Huber, Y. Park, T. Kanade: Fast and accurate computation of surface normals from range images, Proc. IEEE Int. Conf.RoboticsAutom. (ICRA) (2011) pp. 3084–3091

    Google Scholar 

  53. D. Huber: Automatic Three-Dimensional Modeling from Reality, Ph.D. Thesis (Robotics Institute, Carnegie Mellon University, Pittsburg 2002)

    Google Scholar 

  54. R.B. Rusu: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments, Dissertation (TU Munich, Munich 2009)

    Google Scholar 

  55. Point Cloud Library (PCL): http://www.pointclouds.org (2015)

  56. J. Böhm, S. Becker: Automatic marker-free registration of terrestrial laser scans using reflectance features, Proc.8th Conf.Opt. 3D Meas. Tech. (2007) pp. 338–344

    Google Scholar 

  57. N. Engelhard, F. Endres, J. Hess, J. Sturm, W. Burgard: Real-time 3D visual SLAM with a hand-held camera, Proc. RGB-D Workshop3D Percept.Robotics atEur. Robotics Forum (2011)

    Google Scholar 

  58. R. Schnabel, R. Wahl, R. Klein: Efficient RANSAC for point-cloud shape detection, Computer Graph. Forum (2007)

    Google Scholar 

  59. A. Hoover, G. Jean-Baptiste, X. Jiang, P.J. Flynn, H. Bunke, D. Goldgof, K. Bowyer, D. Eggert, A. Fitzgibbon, R. Fisher: An experimental comparison of range segmentation algorithms, IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 673–689 (1996)

    Article  Google Scholar 

  60. U. Bauer, K. Polthier: Detection of planar regions in volume data for topology optimization, Proc. 5th Int. Conf.Adv.Geom. Model.Process. (2008)

    Google Scholar 

  61. P.V.C. Hough: Method and means for recognizing complex patterns, Patent US 3069654 (1962)

    Google Scholar 

  62. D. Borrmann, J. Elseberg, A. Nüchter, K. Lingemann: The 3D Hough transform for plane detection in point clouds – A review and a new accumulator design, J. 3D Res. 2(2), 1–13 (2011)

    Article  Google Scholar 

  63. R. Lakaemper, L.J. Latecki: Extended EM for planar approximation of 3D data, Proc. IEEE Int. Conf.RoboticsAutom. (ICRA) (2006)

    Google Scholar 

  64. O. Wulf, K.O. Arras, H.I. Christensen, B.A. Wagner: 2D Mapping of cluttered indoor environments by means of 3D perception, Proc. IEEE Int. Conf.RoboticsAutom. (ICRA) (2004) pp. 4204–4209

    Google Scholar 

  65. G. Yu, M. Grossberg, G. Wolberg, I. Stamos: Think globally, cluster locally:A unified framework for range segmentation, Proc. 4th Int. Symp.3D Data Process. Vis. Transm. (2008)

    Google Scholar 

  66. W.E. Lorensen, H.E. Cline: Marching Cubes: A high resolution 3D surface construction algorithm, Computer Graph. 21(4), 163–169 (1987)

    Article  Google Scholar 

  67. M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, C.T. Silva: Computing and rendering point set surfaces, IEEE Trans. Vis. Comput. Graph. 9(1), 3–15 (2003)

    Article  Google Scholar 

  68. H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, W. Stuetzle: Surface reconstruction from unorganized points, Comput. Graph. 26(2), 71–78 (1992)

    Article  Google Scholar 

  69. S. Melax: A Simple, fast and effective polygon reduction algorithm, Game Dev. 5(11), 44–49 (1998)

    Google Scholar 

  70. M. Garland, P. Heckbert: Surface simplification using quadric error metrics, Proc.SIGGRAPH (1997)

    Google Scholar 

  71. S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, A. Fitzgibbon: KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera, ACM Symp.User Interface Softw.Technol. (2011)

    Google Scholar 

  72. J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics: Principles and Practice, 2nd edn. (Addison-Wesley, Reading 1996)

    MATH  Google Scholar 

  73. J. Elseberg, D. Borrmann, A. Nüchter: One billion points in the cloud -- An octree for efficient processing of 3D laser scans, ISPRS J. Photogramm.Remote Sens. 76, 76–88 (2013)

    Article  Google Scholar 

  74. A. Hornung, K.M. Wurm, M. Bennewitz, C. Stachniss, W. Burgard: OctoMap: An efficient probabilistic 3D mapping framework based on octrees, Auton. Robots 34(3), 189–206 (2013)

    Article  Google Scholar 

  75. F. Lu, E. Milios: Globally consistent range scan alignment for environment mapping, Auton. Robots 4, 333–349 (1997)

    Article  Google Scholar 

  76. K. Konolige: Large-scale map-making, Proc. Natl. Conf. Artif. Intell. (AAAI) (2004) pp. 457–463

    Google Scholar 

  77. A. Kelly, R. Unnikrishnan: Efficient construction of globally consistent ladar maps using pose network topology and nonlinear programming, Proc. Int. Symp. Robotics Res. (2003)

    Google Scholar 

  78. D. Borrmann, J. Elseberg, K. Lingemann, A. Nüchter, J. Hertzberg: Globally consistent 3d mapping with scan matching, J. RoboticsAuton. Syst. 56(2), 130–142 (2008)

    Google Scholar 

  79. E. Grimson, T. Lozano-Pérez, D.P. Huttenlocher: Object Recognition by Computer: The Role of Geometric Constraints (MIT Press, Cambridge 1990)

    Google Scholar 

  80. Z. Zhang: Parameter estimation techniques: a tutorial with application to conic fitting, ImageVis. Comput., Vol. 15 (1997) pp. 59–76

    Google Scholar 

  81. P. Benko, G. Kos, T. Varady, L. Andor, R.R. Martin: Constrained fitting in reverse engineering, Computer Aided Geom. Des. 19, 173–205 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  82. M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, D. Fulk: The digital Michelangelo project: 3D scanning of large statues, Proc. 27th Conf.Computer Graph.Interact. Tech. (SIGGRAPH) (2000) pp. 131–144

    Google Scholar 

  83. I. Stamos, P. Allen: 3-D model construction using range and image data, Proc. IEEE Conf.Computer Vis.Pattern Recognit., Vol. 1 (2000) pp. 531–536

    Google Scholar 

  84. S. Thrun, W. Burgard, D. Fox: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping, Proc. IEEE Inf. Conf.RoboticsAutom. (2000) pp. 321–328

    Google Scholar 

  85. M. Bosse, R. Zlot, P. Flick: Zebedee: Design of a spring-mounted 3-D range sensor with application to mobile mapping, IEEE Trans. Robotics 28(5), 1104–1119 (2012)

    Article  Google Scholar 

  86. The DARPA Grand Challenge: http://archive.darpa.mil/grandchallenge05/gcorg/index.html (2015)

  87. S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L.-E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, P. Mahoney: Stanley: The robot that won the DARPA grand challenge, J. Field Robot. 23(9), 661–692 (2006)

    Article  Google Scholar 

  88. R. Triebel, P. Pfaff, W. Burgard: Multi-level surface maps for outdoor terrain mapping and loop closing, Proc. IEEE Int. Conf.Intel. RobotsSyst. (IROS) (2006)

    Google Scholar 

  89. C. Eveland, K. Konolige, R. Bolles: Background modeling for segmentation of video-rate stereo sequences, Proc. Int. Conf. Computer Vis.Pattern Recog. (1998) pp. 266–271

    Google Scholar 

  90. M. Agrawal, K. Konolige, L. Iocchi: Real-time detection of independent motion using stereo, IEEE WorkshopMotion (2005) pp. 207–214

    Google Scholar 

  91. K. Konolige, M. Agrawal, R.C. Bolles, C. Cowan, M. Fischler, B. Gerkey: Outdoor mapping and Navigation using stereo vision, Intl. Symp.Exp. Robotics (ISER) (2006)

    Google Scholar 

  92. M. Happold, M. Ollis, N. Johnson: Enhancing supervised terrain classification with predictive unsupervised learning, Robotics: Sci.Syst. Phila. (2006)

    Google Scholar 

  93. J. Lalonde, N. Vandapel, D. Huber, M. Hebert: Natural terrain classification using three-dimensional ladar data for ground robot mobility, J.Field Robotics 23(10), 839–862 (2006)

    Article  Google Scholar 

  94. R. Manduchi, A. Castano, A. Talukder, L. Matthies: Obstacle detection and terrain classification for autonomous off-road navigation, Auton. Robots 18, 81–102 (2005)

    Article  Google Scholar 

  95. J.-F. Lalonde, N. Vandapel, M. Hebert: Data structure for efficient processing in 3-D, Robotics: Sci.Syst. (2005)

    Google Scholar 

  96. A. Kelly, A. Stentz, O. Amidi, M. Bode, D. Bradley, A. Diaz-Calderon, M. Happold, H. Herman, R. Mandelbaum, T. Pilarski, P. Rander, S. Thayer, N. Vallidis, R. Warner: Toward reliable off road autonomous vehicles operating in challenging environments, Int. J.Robotics Res. 25(5/6), 449–483 (2006)

    Article  Google Scholar 

  97. P. Bellutta, R. Manduchi, L. Matthies, K. Owens, A. Rankin: Terrain perception for Demo III, Proc.IEEE Intell. Veh. Conf. (2000) pp. 326–331

    Google Scholar 

  98. KARTO: Software for robots on the move, http://www.kartorobotics.com (2015)

  99. The Stanford Artificial Intelligence Robot: http://www.cs.stanford.edu/group/stair (2015)

  100. Perception for Humanoid Robots: https://www.ri.cmu.edu/research_project_detail.html?project_id=595 (2015)

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Authors and Affiliations

  1. Google, Inc., 1600 Amphitheatre Parkway, CA 94043, Mountain View, USA

    Kurt Konolige

  2. Informatics VII – Robotics and Telematics, University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    Andreas Nüchter

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  1. Kurt Konolige
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  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

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Konolige, K., Nüchter, A. (2016). Range Sensing. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_31

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