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Robotics 2016, 5(4), 25; doi:10.3390/robotics5040025

Improving Robot Mobility by Combining Downward-Looking and Frontal Cameras

1
Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2
Smith-Kettlewell Eye Research Institute, San Francisco, CA 94115 , USA
3
Dpto. de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Zaragoza 50018, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Huosheng Hu
Received: 4 October 2016 / Revised: 9 November 2016 / Accepted: 15 November 2016 / Published: 28 November 2016
View Full-Text   |   Download PDF [7032 KB, uploaded 28 November 2016]   |  

Abstract

This paper presents a novel attempt to combine a downward-looking camera and a forward-looking camera for terrain classification in the field of off-road mobile robots. The first camera is employed to identify the terrain beneath the robot. This information is then used to improve the classification of the forthcoming terrain acquired from the frontal camera. This research also shows the usefulness of the Gist descriptor for terrain classification purposes. Physical experiments conducted in different terrains (quasi-planar terrains) and different lighting conditions, confirm the satisfactory performance of this approach in comparison with a simple color-based classifier based only on frontal images. Our proposal substantially reduces the misclassification rate of the color-based classifier (∼10% versus ∼20%). View Full-Text
Keywords: mobile robotics; computer vision; terrain classification; color and gist descriptors mobile robotics; computer vision; terrain classification; color and gist descriptors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gonzalez, R.; Rituerto, A.; Guerrero, J.J. Improving Robot Mobility by Combining Downward-Looking and Frontal Cameras. Robotics 2016, 5, 25.

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