Affiliation:
1. Royal Institute of Technology – KTH, Sweden
2. Democritus University of Thrace, Greece
Abstract
Traversability estimation is the process of assessing whether a robot is able to move across a specific area. Autonomous robots need to have such an ability to automatically detect and avoid non-traversable areas and, thus, stereo vision is commonly used towards this end constituting a reliable solution under a variety of circumstances. This chapter discusses two different intelligent approaches to assess the traversability of the terrain in front of a stereo vision-equipped robot. First, an approach based on a fuzzy inference system is examined and then another approach is considered, which extracts geometrical descriptions of the scene depth distribution and uses a trained support vector machine (SVM) to assess the traversability. The two methods are presented and discussed in detail.
Reference42 articles.
1. Baudoin, Y., Doroftei, D., De Cubber, G., Berrabah, S. A., Pinzon, C., Warlet, F., et al. (2009). View-finder: Robotics assistance to fire-fighting services and crisis management. In IEEE International Workshop on Safety, Security, and Rescue Robotics (pp. 1–6). Denver, Colorado, USA.
2. Real-time obstacle avoidance for fast mobile robots
3. The vector field histogram-fast obstacle avoidance for mobile robots
4. De Cubber, G., Doroftei, D., Nalpantidis, L., Sirakoulis, G. C., & Gasteratos, A. (2009). Stereo- based terrain traversability analysis for robot navigation. In IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance. Brussels, Belgium.
5. A fast area-based stereo matching algorithm