Skip to main content
SpringerLink
Log in

Range Sensors

  • Reference work entry
Springer Handbook of Robotics

Abstract

Range sensors are devices that capture the three-dimensional (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 reasonably 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. 22.1 – stereo and laser triangulation and ranging systems), how multiple observations of the scene, e.g., as if from a moving robot, can be registered (Sect. 22.2), and several indoor and outdoor robot applications where range data greatly simplifies the task (Sect. 22.3).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 309.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ASIC:

application-specific integrated circuit

CP:

cerebral palsy

CP:

closest point

CP:

complementarity problem

DARPA:

Defense Advanced Research Projects Agency

FPGAs:

field programmable gate array

GPS:

global positioning system

ICP:

iterative closest-point algorithm

LADAR:

laser radar or laser detection and ranging

LIDAR:

light detection and ranging

MLS:

multilevel surface map

PC:

Purkinje cells

PC:

principal contact

RANSAC:

random sample consensus

RGB:

red, green, blue

SIFT:

scale-invariant feature transformation

SLAM:

simultaneous localization and mapping

References

  1. Videre Design LLC: www.videredesign.com, accessed Nov 12, 2007 (Videre Design, Menlo Park 2007)

  2. R. Hartley, A. Zisserman: Multiple view geometry in computer vision (Cambridge Univ. Press, Cambridge 2000)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  4. K. Konolige: Small vision system. hardware and implementation, Proc. Int. Symp. Robot. Res. (Hayama 1997) pp. 111–116

    Google Scholar 

  5. D. Scharstein, R. Szeliski, R. Zabih: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Int. J. Comput. Vis. 47(1/2/3), 7–42 (2002)

    Article  MATH  Google Scholar 

  6. D. Scharstein, R. Szeliski: Middlebury College Stereo Vision Research Page, vision.middlebury.edu/stereo, accessed Nov 12, 2007 (Middleburry College, Middleburry 2007)

  7. R. Zabih, J. Woodfill: Non-parametric local transforms for computing visual correspondence, Proc. Eur. Conf. on Computer Vision, Vol. 2 (Stockholm 1994) pp. 151–158

    Google Scholar 

  8. 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, Tech. Report RR-2013, INRIA (1993)

    Google Scholar 

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

    Article  Google Scholar 

  10. 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 

  11. R. Bolles, J. Woodfill: Spatiotemporal consistency checking of passive range data, Proc. Int. Symp. on Robotics Research (Hidden Valley 1993)

    Google Scholar 

  12. 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 

  13. H. Moravec: Visual mapping by a robot rover, Proc. Int. Joint Conf. on AI (IJCAI) (Tokyo 1979) pp. 598–600

    Google Scholar 

  14. A. Adan, F. Molina, L. Morena: Disordered patterns projection for 3D motion recovering, Proc. Int. Conf. on 3D Data Processing, Visualization and Transmission (Thessaloniki 2004) pp. 262–269

    Google Scholar 

  15. Point Grey Research Inc.: www.ptgrey.com, accessed Nov 12, 2007 (Point Grey Research, Vancouver 2007)

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

    Google Scholar 

  17. R. Yang, M. Pollefeys: Multi-resolution real-time stereo on commodity graphics hardware, Int. Conf. Computer Vision and Pattern Recognition, Vol. 1 (Madison 2003) pp. 211–217

    Google Scholar 

  18. Focus Robotics Inc.: www.focusrobotics.com, accessed Nv 12, 2007 (Focus Robotics, Hudson 2007)

  19. TYZX Inc.: www.tyzx.com, accessed Nov 12, 2007 (TYZX, Menlo Park 2007)

  20. S.K. Nayar, Y. Nakagawa: Shape from focus, IEEE Trans. Patt. Anal. Mach. Intell. 16(8), 824–831 (1994)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. F. Blais: Review of 20 years of range sensor development, J. Electron. Imag. 13(1), 231–240 (2004)

    Article  Google Scholar 

  25. R. Baribeau, M. Rioux, G. Godin: Color reflectance modeling using a polychromatic laser range sensor, IEEE Trans. Patt. Anal. Mach. Intell. 14(2), 263–269 (1992)

    Article  Google Scholar 

  26. D. Anderson, H. Herman, A. Kelly: Experimental Characterization of Commercial Flash Ladar Devices, Int. Conf. of Sensing and Technology (Palmerston North 2005) pp. 17–23

    Google Scholar 

  27. R. Stettner, H. Bailey, S. Silverman: Three-Dimensional Flash Ladar Focal Planes and Time-Dependent Imaging, Advanced Scientific Concepts, 2006; Technical Report (February 23, 2007): www.advancedscientificconcepts.com/images/Three Dimensional Flash Ladar Focal Planes-ISSSR Paper.pdf, accessed Nov 12, 2007 (Advanced Scientific Concepts, Santa Barbara 2007)

  28. J.J. LeMoigne, A.M. Waxman: Structured light patterns for robot mobility, Robot. Autom. 4, 541–548 (1988)

    Article  Google Scholar 

  29. R.B. Fisher, D.K. Naidu: A Comparison of Algorithms for Subpixel Peak Detection. In: Image Technology, ed. by J. Sanz (Springer, Berlin, Heidelberg 1996)

    Google Scholar 

  30. J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics: principles and practice (Addison Wesley, Reading 1996)

    MATH  Google Scholar 

  31. B. Curless, M. Levoy: A Volumetric Method for Building Complex Models from Range Images, Proc. of Int. Conf. on Comput. Graph. and Inter. Tech. (SIGGRAPH) (New Orleans 1996) pp. 303–312

    Google Scholar 

  32. 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. Patt. Anal. Mach. Intell. 18(7), 673–689 (1996)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  34. 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 

  35. A. Hilton, A. Stoddart, J. Illingworth, T. Windeatt: Implicit surface-based geometric fusion, Comput. Vis. Image Under. 69(3), 273–291 (1998)

    Article  Google Scholar 

  36. H. Hoppe: New quadric metric for simplifying meshes with appearance attributes, IEEE Visualization 1999 Conference (San Francisco 1999) pp. 59–66

    Google Scholar 

  37. W.J. Schroeder, J.A. Zarge, W.E. Lorensen: Decimation of triangle meshes, Proc. of Int. Conf. on Comput. Graph. and Inter. Tech. (SIGGRAPH) (Chicago 1992) pp. 65–70

    Google Scholar 

  38. S. Thrun: A probabilistic online mapping algorithm for teams of mobile robots, Int. J. Robot. Res. 20(5), 335–363 (2001)

    Article  Google Scholar 

  39. J. Little, S. Se, D. Lowe: Vision based mobile robot localization and mapping using scale-invariant features, Proc. IEEE Inf. Conf. on Robotics and Automation (Seoul 2001) pp. 2051–2058

    Google Scholar 

  40. E. Grimson: Object Recognition by Computer: The Role of Geometric Constraints (MIT Press, London 1990)

    Google Scholar 

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

    Article  Google Scholar 

  42. G. Turk, M. Levoy: Zippered Polygon Meshes from Range Images, Proc. of Int. Conf. on Comput. Graph. and Inter. Tech. (SIGGRAPH) (Orlando 1994) pp. 311–318

    Google Scholar 

  43. S. Thrun, W. Burgard, D. Fox: Probabilistic Robotics (MIT Press, Cambridge 2005)

    MATH  Google Scholar 

  44. D. Haehnel, D. Schulz, W. Burgard: Mapping with mobile robots in populated environments, Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Vol. 1 (Lausanne 2002) pp. 496–501

    Google Scholar 

  45. K. Konolige, K. Chou: Markov localization using correlation, Proc. Int. Joint Conf. on AI (IJCAI) (Stockholm 1999) pp. 1154–1159

    Google Scholar 

  46. D. Haehnel, W. Burgard: Probabilistic Matching for 3D Scan Registration, Proc. of the VDI-Conference Robotik 2002 (Robotik) (Ludwigsburg 2002)

    Google Scholar 

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

    Article  Google Scholar 

  48. K. Konolige: Large-scale map-making, Proceedings of the National Conference on AI (AAAI) (San Jose 2004) pp. 457–463

    Google Scholar 

  49. A. Kelly, R. Unnikrishnan: Efficient Construction of Globally Consistent Ladar Maps using Pose Network Topology and Nonlinear Programming, Proc. Int. Symp of Robotics Research (Siena 2003)

    Google Scholar 

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

    Article  Google Scholar 

  51. Z. Zhang: Parameter estimation techniques: a tutorial with application to conic fitting, Image Vis. Comput. 15, 59–76 (1997)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  53. 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. on Computer graphics and interactive techniques (SIGGRAPH) (New Orleans 2000) pp. 131–144

    Google Scholar 

  54. I. Stamos, P. Allen: 3-D Model Construction Using Range and Image Data, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1 (Hilton Head Island 2000) pp. 531–536

    Google Scholar 

  55. R. Triebel, P. Pfaff, W. Burgard: Multi-level surface maps for outdoor terrain mapping and loop closing, Proc. of the IEEE Int. Conf. on Intel. Robots and Systems (IROS) (Beijing 2006)

    Google Scholar 

  56. 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. on Robotics and Automation (San Francisco 2000) pp. 321–328

    Google Scholar 

  57. Y. Liu, R. Emery, D. Chakrabarti, W. Burgard, S. Thrun: Using EM to Learn 3D Models of Indoor Environments with Mobile Robots, Proc.Int. Conf. on Machine Learning (Williamstown 2001) pp. 329–336

    Google Scholar 

  58. M. Agrawal, K. Konolige, L. Iocchi: Real-time detection of independent motion using stereo, IEEE Workshop on Motion (Breckenridge 2005) pp. 207–214

    Google Scholar 

  59. The DARPA Grand Challenge: www.darpa.mil/grandchallenge05, accessed Nov 12, 2007 (DARPA, Arlington 2005)

  60. S. Thrun, M. Montemerlo, H. Dahlkamp et al.: Stanley: The robot that won the DARPA Grand Challenge, J. Field Robot. 23(9), 661–670 (2006)

    Article  Google Scholar 

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

    Google Scholar 

  62. K. Konolige, M. Agrawal, R.C. Bolles, C. Cowan, M. Fischler, B. Gerkey: Outdoor mapping and Navigation using Stereo Vision, Intl. Symp. on Experimental Robotics (ISER) (Rio de Janeiro 2006)

    Google Scholar 

  63. J. Lalonde, N. Vandapel, D. Huber, M. Hebert: Natural terrain classification using three-dimensional ladar data for ground robot mobility, J. Field Robot. 23(10), 839–861 (2006)

    Article  Google Scholar 

  64. J.-F. Lalonde, N. Vandapel, M. Hebert: Data structure for efficient processing in 3-D, Robotics: Science and Systems 1, Cambridge (2005)

    Google Scholar 

  65. M. Happold, M. Ollis, N. Johnson: enhancing supervised terrain classification with predictive unsupervised learning, Robotics: Science and Systems (Philadelphia 2006)

    Google Scholar 

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

    Article  Google Scholar 

  67. 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. Robot. Res. 25(5–6), 449–483 (2006)

    Article  Google Scholar 

  68. P. Bellutta, R. Manduchi, L. Matthies, K. Owens, A. Rankin: Terrain Perception for Demo III, Proc. of the 2000 IEEE Intelligent Vehicles Conf. (Dearborn 2000) pp. 326–331

    Google Scholar 

  69. KARTO: Software for robots on the move. www.kartorobotics.com, accessed Nov 12, 2007 (ISRI, Menlo Park 2007)

  70. The Stanford Artificial Intelligence Robot: www.cs.stanford.edu/group/stair, accessed Nov 12, 2007 (Stanford Univ., Stanford 2007)

  71. Perception for Humanoid Robots. www.ri.cmu.edu/projects/project_595.html, accessed Nov 12, 2007 (Carnegie Melon Univ., Pittsburgh 2007)

Download references

Author information

Authors and Affiliations

  1. School of Informatics, University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, EH9 3JZ, Edinburgh, UK

    Robert B. Fisher PhD

  2. Artificial Intelligence Center, SRI International, 333 Ravenswood Ave., 94025, Menlo Park, CA, USA

    Kurt Konolige Prof

Authors
  1. Robert B. Fisher PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kurt Konolige Prof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Robert B. Fisher PhD or Kurt Konolige Prof .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Fisher, R.B., Konolige, K. (2008). Range Sensors. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30301-5_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23957-4

  • Online ISBN: 978-3-540-30301-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation