Autonomous color learning on a mobile robot

Abstract

Color segmentation is a challenging subtask in computer vi-sion. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a sta-tionary camera. This paper presents an approach for color learn-ing 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.