Robust and real-time self-localization based on omnidirectional vision for soccer robots

Abstract

Self-Localization is the basis to realize autonomous ability such as motion planning and decision making for mobile robots, and omnidirectional vision is one of the most important sensors for RoboCup Middle Size League (MSL) soccer robots. According to the characteristic that RoboCup competition is highly dynamic and the deficiency of the current self-localization methods, a robust and real-time self-localization algorithm based on omnidirectional vision is proposed for MSL soccer robots. Monte Carlo localization and matching optimization localization, two most popular approaches used in MSL, are combined in our algorithm. The advantages of these two approaches are maintained, while the disadvantages are avoided. A camera parameters auto-adjusting method based on image entropy is also integrated to adapt the output of omnidirectional vision to dynamic lighting conditions. The experimental results show that global localization can be realized effectively while highly accurate localization is achieved in real-time, and robot self-localization is robust to the highly dynamic environment with occlusions and changing lighting conditions.

Fútbol de robots: orígenes, federaciones, ligas y horizontes de investigación

“…Otros trabajos se enfocaban en la estimación de movimientos de objetos [46], tomando en este caso el balón y buscando estimar su velocidad y posibles colisiones, por medio de algoritmos de regresión Ridge en este caso. Se realizan investigaciones en autolocalización visual, especialmente utilizado en ligas con percepción distribuida combinando por ejemplo, localización Montecarlo con localización por optimización de ajuste [47].…”

A Survey on Visual Perception for RoboCup MSL Soccer Robots

“…The NuBot team proposed a self-localization algorithm by combining these two approaches to maintain their advantages and avoid the disadvantages [57, 58]. Firstly, MCL was used to achieve global localization and then matching optimization localization was applied to realize accurate and efficient localization tracking with the result of MCL as the initial localization value.…”