Performance Evaluation of a Terrain Traversability Learning Algorithm in the DARPA LAGR Program
NATIONAL INST OF STANDARDS AND TECHNOLOGY GAITHERSBURG MD
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AbstractThe 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, The National Institute of Standards and Technology NIST has embedded learning into a control system architecture called 4DRCS 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 vehicles 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.
- Numerical Mathematics
- Computer Programming and Software
- Surface Transportation and Equipment
- Navigation and Guidance