Real-time, adaptive color-based robot vision

With the wide availability, high information content, and suitability for human environments of low-cost color cameras, machine vision is an appealing sensor for many robot platforms. For researchers interested in autonomous robot teams operating in highly dynamic environments performing complex tasks, such as robot soccer, fast color-based object recognition is very desirable. Indeed, there are a number of existing algorithms that have been developed within the community to achieve this goal. Many of these algorithms, however, do not adapt for variation in lighting intensity, thereby limiting their use to statically and uniformly lit indoor environments. In this paper, we present a new technique for color object recognition that can adapt to changes in illumination but remains computationally efficient. We present empirical results demonstrating the performance of our technique for both indoor and outdoor environments on a robot platform performing tasks drawn from the robot soccer domain. Additionally, we compare the computational speed of our new approach against CMVision, a fast open-source color segmentation library. Our performance results show that our technique is able to adapt to lighting variations without requiring significant additional CPU resources.