Evaluation of Self-localization Performance for a Local Vision Robot in the Small Size League
For accurate self-localization using probabilistic techniques, robots require robust models of motion and sensor characteristics. Such models are sensitive to variations in lighting conditions, terrain and other factors like robot battery strength. Each of these factors can introduce variations in the level of noise considered by probabilistic techniques. Manually constructing models of noise is time-consuming, tedious and error-prone. We have been developing techniques for automatically acquiring such models, using the AIBO robot and a modified RoboCup Four-Legged League field with an overhead camera. This paper describes our techniques and presents preliminary results.