Support Vector Machines and Features for Environment Perception in Mobile Robotics

  • Rui Araújo
  • Urbano Nunes
  • Luciano Oliveira
  • Pedro Sousa
  • Paulo Peixoto
Part of the Studies in Computational Intelligence book series (SCI, volume 137)


Environment perception is one of the most challenging and underlying task which allows a mobile robot to perceive obstacles, landmarks and extract useful information to navigate safely. In this sense, classification techniques applied to sensor data may enhance the way mobile robots sense their surroundings. Amongst several techniques to classify data and to extract relevant information from the environment, Support Vector Machines (SVM) have demonstrated promising results, being used in several practical approaches. This chapter presents the core theory of SVM, and applications in two different scopes: using Lidar (Light Detection and Ranging) to label specific places, and vision-based human detection aided by Lidar.


Support Vector Machine Input Space Support Vector Machine Model Radial Basis Function Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rui Araújo
    • 1
  • Urbano Nunes
    • 1
  • Luciano Oliveira
    • 1
  • Pedro Sousa
    • 1
  • Paulo Peixoto
    • 1
  1. 1.ISR-Institute of Systems and Robotics, and Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

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