Springer Handbook of Robotics pp 521-542 | Cite as
Range Sensors
- 11 Citations
- 49k Downloads
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).
Keywords
Range Image Iterate Close Point Range Sensor Iterate Close Point Robotic ApplicationAbbreviations
- 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
- 22.1.Videre Design LLC: www.videredesign.com, accessed Nov 12, 2007 (Videre Design, Menlo Park 2007)
- 22.2.R. Hartley, A. Zisserman: Multiple view geometry in computer vision (Cambridge Univ. Press, Cambridge 2000)zbMATHGoogle Scholar
- 22.3.S. Barnard, M. Fischler: Computational stereo, ACM Comput. Surv. 14(4), 553–572 (1982)CrossRefGoogle Scholar
- 22.4.K. Konolige: Small vision system. hardware and implementation, Proc. Int. Symp. Robot. Res. (Hayama 1997) pp. 111–116Google Scholar
- 22.5.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)CrossRefzbMATHGoogle Scholar
- 22.6.D. Scharstein, R. Szeliski: Middlebury College Stereo Vision Research Page, vision.middlebury.edu/stereo, accessed Nov 12, 2007 (Middleburry College, Middleburry 2007)
- 22.7.R. Zabih, J. Woodfill: Non-parametric local transforms for computing visual correspondence, Proc. Eur. Conf. on Computer Vision, Vol. 2 (Stockholm 1994) pp. 151–158Google Scholar
- 22.8.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
- 22.9.M. Okutomi, T. Kanade: A multiple-baseline stereo, IEEE Trans. Patt. Anal. Mach. Intell. 15(4), 353–363 (1993)CrossRefGoogle Scholar
- 22.10.L. Matthies: Stereo vision for planetary rovers: stochastic modeling to near realtime implementation, Int. J. Comput. Vis. 8(1), 71–91 (1993)CrossRefMathSciNetGoogle Scholar
- 22.11.R. Bolles, J. Woodfill: Spatiotemporal consistency checking of passive range data, Proc. Int. Symp. on Robotics Research (Hidden Valley 1993)Google Scholar
- 22.12.P. Fua: A parallel stereo algorithm that produces dense depth maps and preserves image features, Mach. Vis. Appl. 6(1), 35–49 (1993)CrossRefGoogle Scholar
- 22.13.H. Moravec: Visual mapping by a robot rover, Proc. Int. Joint Conf. on AI (IJCAI) (Tokyo 1979) pp. 598–600Google Scholar
- 22.14.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. 262–269Google Scholar
- 22.15.Point Grey Research Inc.: www.ptgrey.com, accessed Nov 12, 2007 (Point Grey Research, Vancouver 2007)
- 22.16.C. Zach, A. Klaus, M. Hadwiger, K. Karner: Accurate dense stereo reconstruction using graphics hardware, Proc. EUROGRAPHICS (Granada 2003) pp. 227–234Google Scholar
- 22.17.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. 211–217Google Scholar
- 22.18.Focus Robotics Inc.: www.focusrobotics.com, accessed Nv 12, 2007 (Focus Robotics, Hudson 2007)
- 22.19.TYZX Inc.: www.tyzx.com, accessed Nov 12, 2007 (TYZX, Menlo Park 2007)
- 22.20.S.K. Nayar, Y. Nakagawa: Shape from focus, IEEE Trans. Patt. Anal. Mach. Intell. 16(8), 824–831 (1994)CrossRefGoogle Scholar
- 22.21.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)CrossRefGoogle Scholar
- 22.22.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)CrossRefGoogle Scholar
- 22.23.A. Lobay, D.A. Forsyth: Shape from texture without boundaries, Int. J. Comput. Vis. 67(1), 71–91 (2006)CrossRefGoogle Scholar
- 22.24.F. Blais: Review of 20 years of range sensor development, J. Electron. Imag. 13(1), 231–240 (2004)CrossRefGoogle Scholar
- 22.25.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)CrossRefGoogle Scholar
- 22.26.D. Anderson, H. Herman, A. Kelly: Experimental Characterization of Commercial Flash Ladar Devices, Int. Conf. of Sensing and Technology (Palmerston North 2005) pp. 17–23Google Scholar
- 22.27.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)
- 22.28.J.J. LeMoigne, A.M. Waxman: Structured light patterns for robot mobility, Robot. Autom. 4, 541–548 (1988)CrossRefGoogle Scholar
- 22.29.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
- 22.30.J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics: principles and practice (Addison Wesley, Reading 1996)zbMATHGoogle Scholar
- 22.31.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. 303–312Google Scholar
- 22.32.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)CrossRefGoogle Scholar
- 22.33.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)CrossRefMathSciNetGoogle Scholar
- 22.34.H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, W. Stuetzle: Surface reconstruction from unorganized points, Comput. Graph. 26(2), 71–78 (1992)CrossRefGoogle Scholar
- 22.35.A. Hilton, A. Stoddart, J. Illingworth, T. Windeatt: Implicit surface-based geometric fusion, Comput. Vis. Image Under. 69(3), 273–291 (1998)CrossRefGoogle Scholar
- 22.36.H. Hoppe: New quadric metric for simplifying meshes with appearance attributes, IEEE Visualization 1999 Conference (San Francisco 1999) pp. 59–66Google Scholar
- 22.37.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. 65–70Google Scholar
- 22.38.S. Thrun: A probabilistic online mapping algorithm for teams of mobile robots, Int. J. Robot. Res. 20(5), 335–363 (2001)CrossRefGoogle Scholar
- 22.39.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. 2051–2058Google Scholar
- 22.40.E. Grimson: Object Recognition by Computer: The Role of Geometric Constraints (MIT Press, London 1990)Google Scholar
- 22.41.P.J. Besl, N.D. McKay: A method for registration of 3D shapes, IEEE Trans. Patt. Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
- 22.42.G. Turk, M. Levoy: Zippered Polygon Meshes from Range Images, Proc. of Int. Conf. on Comput. Graph. and Inter. Tech. (SIGGRAPH) (Orlando 1994) pp. 311–318Google Scholar
- 22.43.S. Thrun, W. Burgard, D. Fox: Probabilistic Robotics (MIT Press, Cambridge 2005)zbMATHGoogle Scholar
- 22.44.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. 496–501Google Scholar
- 22.45.K. Konolige, K. Chou: Markov localization using correlation, Proc. Int. Joint Conf. on AI (IJCAI) (Stockholm 1999) pp. 1154–1159Google Scholar
- 22.46.D. Haehnel, W. Burgard: Probabilistic Matching for 3D Scan Registration, Proc. of the VDI-Conference Robotik 2002 (Robotik) (Ludwigsburg 2002)Google Scholar
- 22.47.F. Lu, E. Milios: Globally consistent range scan alignment for environment mapping, Auton. Robot. 4, 333–349 (1997)CrossRefGoogle Scholar
- 22.48.K. Konolige: Large-scale map-making, Proceedings of the National Conference on AI (AAAI) (San Jose 2004) pp. 457–463Google Scholar
- 22.49.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
- 22.50.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)CrossRefGoogle Scholar
- 22.51.Z. Zhang: Parameter estimation techniques: a tutorial with application to conic fitting, Image Vis. Comput. 15, 59–76 (1997)CrossRefGoogle Scholar
- 22.52.P. Benko, G. Kos, T. Varady, L. Andor, R.R. Martin: Constrained fitting in reverse engineering, Comput. Aided Geom. Des. 19, 173–205 (2002)CrossRefMathSciNetGoogle Scholar
- 22.53.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. 131–144Google Scholar
- 22.54.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. 531–536Google Scholar
- 22.55.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
- 22.56.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. 321–328Google Scholar
- 22.57.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. 329–336Google Scholar
- 22.58.M. Agrawal, K. Konolige, L. Iocchi: Real-time detection of independent motion using stereo, IEEE Workshop on Motion (Breckenridge 2005) pp. 207–214Google Scholar
- 22.59.The DARPA Grand Challenge: www.darpa.mil/grandchallenge05, accessed Nov 12, 2007 (DARPA, Arlington 2005)
- 22.60.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)CrossRefGoogle Scholar
- 22.61.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. 266–271Google Scholar
- 22.62.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
- 22.63.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)CrossRefGoogle Scholar
- 22.64.J.-F. Lalonde, N. Vandapel, M. Hebert: Data structure for efficient processing in 3-D, Robotics: Science and Systems 1, Cambridge (2005)Google Scholar
- 22.65.M. Happold, M. Ollis, N. Johnson: enhancing supervised terrain classification with predictive unsupervised learning, Robotics: Science and Systems (Philadelphia 2006)Google Scholar
- 22.66.R. Manduchi, A. Castano, A. Talukder, L. Matthies: Obstacle detection and terrain classification for autonomous off-road navigation, Auton. Robot. 18, 81–102 (2005)CrossRefGoogle Scholar
- 22.67.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)CrossRefGoogle Scholar
- 22.68.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. 326–331Google Scholar
- 22.69.KARTO: Software for robots on the move. www.kartorobotics.com, accessed Nov 12, 2007 (ISRI, Menlo Park 2007)
- 22.70.The Stanford Artificial Intelligence Robot: www.cs.stanford.edu/group/stair, accessed Nov 12, 2007 (Stanford Univ., Stanford 2007)
- 22.71.Perception for Humanoid Robots. www.ri.cmu.edu/projects/project_595.html, accessed Nov 12, 2007 (Carnegie Melon Univ., Pittsburgh 2007)