Skip to main content
Springer Nature Link
Log in

Obstacle Detection

  • Reference work entry
  • First Online:
Computer Vision

Synonyms

Hazard detection

Definition

Obstacle detection is the process of using sensors, data structures, and algorithms to detect objects or terrain types that impede motion.

Background

Obstacle detection is applicable to anything that moves, including robot manipulators and manned or unmanned vehicles for land, sea, air, and space; for brevity, these are all called vehicleshere. Obstacle detection and hazard detection are synonymous terms, but are sometimes applied in different domains; for example, obstacle detection is usually applied to ground vehicle navigation, whereas hazard detection is often applied to aircraft or spacecraft in the process of landing, as in “landing hazard detection.” Obstacle detection is a system problem that encompasses sensors that perceive the world, world models that represent the sensor data in a convenient form, mathematical models of the interaction between objects and the vehicle, and algorithms that process all of this to infer obstacle...

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as 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

References

  1. Everett HR (1995) Sensors for mobile robots: theory and application. A.K. Peters, Wellesley, MA

    Google Scholar 

  2. Matthies L, Bergh C, Castano A, Macedo J, Manduchi R (2005) Obstacle detection in foliage with ladar and radar. In: Dario P, Chatila R (eds) Robotics research: the eleventh international symposium. Springer, Berlin, pp 291–302

    Google Scholar 

  3. Matthies L, Kelly A, Litwin T, Tharp G (1996) Obstacle detection for unmanned ground vehicles: a progress report. In: Giralt G (ed) Robotics research: the seventh international symposium. Springer, Berlin, pp 475–486

    Google Scholar 

  4. Lalonde J-F, Vandapel N, Hebert M (2007) Data structures for efficient dynamic processing in 3-D. Int J Robot Res 26(8):777–796

    Article  Google Scholar 

  5. . Moravec H, Elfes AE (1985) High resolution maps from wide angle sonar. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), St. Louis, pp 116–121

    Google Scholar 

  6. . Wurm KM, Hornung A, Bennewitz M, Stachniss C, Burgard W (2010) OctoMap: a probabilistic, flexible, and compact 3D map representation for robotic systems. In: Proceedings of the workshop on best practice in 3D perception and modeling for mobile manipulation, Anchorage

    Google Scholar 

  7. Bajracharya M, Moghaddam B, Howard A, Brennan S, Matthies L (2009) A fast stereo-based system for detecting and tracking pedestrians from a moving vehicle. Int J Robot Res 29(11–12):1466–1485

    Article  Google Scholar 

  8. Stentz A, Hebert M (1995) A complete navigation system for goal acquisition in unknown environments. Auton Robot 2(2):127–145

    Article  Google Scholar 

  9. . Iagnemma K et al (2011) Terramechanics modelling of Mars surface exploration rovers for simulation and parameter estimation. In: Proceedings of the ASME international design engineering technical conference, Washington, DC

    Google Scholar 

  10. Olin KE, Tseng DT (1991) Autonomous cross-country navigation: an integrated perception and planning system. IEEE Expert 6(4):16–30

    Article  Google Scholar 

  11. . Trease B et al (2011) Dynamic modelling and soil mechanics for path planning of the Mars Exploration Rovers. In: Proceedings of the ASME international design engineering technical conference, Washington, DC

    Google Scholar 

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

    Article  Google Scholar 

  13. . Rankin AL, Huertas A, Matthies L (2009) Stereo vision based terrain mapping for off-road autonomous navigation. In: Proceedings of the SPIE symposium on unmanned systems technology XI. Orlando, Florida, USA

    Book  Google Scholar 

  14. . Matthies L, Rankin A (2003) Negative obstacle detection by thermal signature. In: Proceedings of the IEEE/RSJ conference on intelligent robots and systems (IROS), Las Vegas

    Google Scholar 

  15. . Matthies L, Bellutta P, McHenry M (2003) Detecting water hazards for autonomous off-road navigation. In: Proceedings of the SPIE symposium on unmanned ground vehicles V. Orlando, Florida, USA

    Google Scholar 

  16. Bajracharya M, Howard A, Matthies L, Tang B, Turmon M (2009) Autonomous off-road navigation with end-to-end learning for the LAGR program. J Field Robot 26(1):3–25

    Article  MATH  Google Scholar 

  17. . Howard TM (2009) Adaptive model-predictive motion planning for navigation in complex environments. Ph.D. thesis, Carnegie Mellon University, CMU-RI-TR-09-32

    Google Scholar 

  18. Rankin A, Matthies L (2010) Passive sensor evaluation for unmanned ground vehicle mud detection. J Field Robot 27(4):473–490

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Larry Matthies

Authors
  1. Larry Matthies
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Katsushi Ikeuchi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Matthies, L. (2014). Obstacle Detection. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_52

Download citation

Publish with us

Policies and ethics