By Topic

Statistical Analysis of Image-Features Used as Inputs of an Road Identifier Based in Artificial Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Patrick Yuri Shinzato ; Inst. of Math. & Comput. Sci., Univ. of Sao Paulo ICMC-USP, Sao Carlos, Brazil ; Denis Fernando Wolf

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community. In order to execute a autonomous driving on outdoors, like street and roads, it is necessary that the vehicle identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of many multi-layer perceptron neural networks(ANN) used for image-based terrain identification. The ANNs differ in image features used in input layer. Experimental tests using a car and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Published in:

Robotics Symposium and Intelligent Robotic Meeting (LARS), 2010 Latin American

Date of Conference:

23-28 Oct. 2010