Collision risk assessment for autonomous robots by offline traversability learning

  • Authors:

  • Ioannis Kostavelis;Lazaros Nalpantidis;Antonios Gasteratos

  • Affiliations:

  • Robotics and Automation Lab., Production and Management Engineering Department, Democritus University of Thrace, Vas. Sofias 12, GR-671 00 Xanthi, Greece;Computer Vision and Active Perception Lab., Centre for Autonomous Systems, Royal Institute of Technology - KTH, SE-100 44 Stockholm, Sweden;Robotics and Automation Lab., Production and Management Engineering Department, Democritus University of Thrace, Vas. Sofias 12, GR-671 00 Xanthi, Greece

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2012

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Abstract

Autonomous robots should be able to move freely in unknown environments and avoid impacts with obstacles. The overall traversability estimation of the terrain and the subsequent selection of an obstacle-free route are prerequisites of a successful autonomous operation. This work proposes a computationally efficient technique for the traversability estimation of the terrain, based on a machine learning classification method. Additionally, a new method for collision risk assessment is introduced. The proposed system uses stereo vision as a first step in order to obtain information about the depth of the scene. Then, a v-disparity image calculation processing step extracts information-rich features about the characteristics of the scene, which are used to train a support vector machine (SVM) separating the traversable and non-traversable scenes. The ones classified as traversable are further processed exploiting the polar transformation of the depth map. The result is a distribution of obstacle existence likelihoods for each direction, parametrized by the robot's embodiment.