Mohan Sridharan, Peter Stone
Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.
Content Area: 17.Robotics
Subjects: 19. Vision; 17. Robotics
Submitted: May 10, 2005