By Topic

Traversability classification using unsupervised on-line visual learning for outdoor robot navigation

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

5 Author(s)
Dongshin Kim ; Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA ; Jie Sun ; Sang Min Oh ; J. M. Rehg
more authors

Estimating the traversability of terrain in an unstructured outdoor environment is a core functionality for autonomous robot navigation. While general-purpose sensing can be used to identify the existence of terrain features such as vegetation and sloping ground, the traversability of these regions is a complex function of the terrain characteristics and vehicle capabilities, which makes it extremely difficult to characterize a priori. Moreover, it is difficult to find general rules which work for a wide variety of terrain types such as trees, rocks, tall grass, logs, and bushes. As a result, methods which provide traversability estimates based on predefined terrain properties such as height or shape will be unlikely to work reliably in unknown outdoor environments. Our approach is based on the observation that traversability in the most general sense is an affordance which is jointly determined by the vehicle and its environment. We describe a novel on-line learning method which can make accurate predictions of the traversability properties of complex terrain. Our method is based on autonomous training data collection which exploits the robot's experience in navigating its environment to train classifiers without human intervention. This is in contrast to other learning methods in which training data is collected manually. We have implemented and tested our traversability learning method on an unmanned ground vehicle (UGV) and evaluated its performance in several realistic outdoor environments. The experiments quantify the benefit of our on-line traversability learning approach

Published in:

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

Date of Conference:

15-19 May 2006