Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Performance Evaluation of a Terrain Traversability Learning Algorithm in The DARPA LAGR Program

Published

Author(s)

Michael O. Shneier, William P. Shackleford, Tsai Hong Hong, Tommy Chang

Abstract

The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. For the LAGR program, NIST has embedded learning into a control system architecture called 4D/RCS to enable the small robot used in the program to learn to navigate through a range of terrain types. This paper describes performance evaluation experiments on one of the algorithms developed under the program to learn terrain traversability. The algorithm uses color and texture to build models describing regions of terrain seen by the vehicle s stereo cameras. Range measurements from stereo are used to assign traversability measures to the regions. The assumption is made that regions that look alike have similar traversability. Thus, regions that match one of the models inherit the traversability stored in the model. This allows all areas of images seen by the vehicle to be classified, and enables a path planner to determine a traversable path to the goal.The algorithm is evaluated by comparison with ground truth generated by a human observer. A graphical user interface (GUI) was developed that displays an image and randomly generates a point to be classified. The human assigns a traversability label to the point, and the learning algorithm associates its own label with the point. When a large number of such points have been labeled across a sequence of images, the performance of the learning algorithm is determined in terms of error rates. The learning algorithm is outlined in the paper, and results of performance evaluation are described.
Proceedings Title
Proceedings of the Performance Metrics for Intelligent Systems Workshop| 2006 | PerMIS
Conference Dates
August 21-23, 2006
Conference Location
Gaithersburg, MD
Conference Title
Performance Metrics for Intelligent Systems (PerMIS) Workshop 2006

Keywords

computer vision, LAGR, learning, performance evaluation, robotics, traversability

Citation

Shneier, M. , Shackleford, W. , , T. and Chang, T. (2007), Performance Evaluation of a Terrain Traversability Learning Algorithm in The DARPA LAGR Program, Proceedings of the Performance Metrics for Intelligent Systems Workshop| 2006 | PerMIS, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=823591 (Accessed June 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created January 31, 2007, Updated February 19, 2017