Abstract: Self-localization is one of the key technologies in soccer robot system. However in the soccer robot system based on the omni-vision, it is hard to match the features extracted from the image to the real features existing in the world due to the large distortion in omni-vision images. This has become the main obstacle to precise self-localization based on the omni-vision.
Vision Based Self Localization for Humanoid Robot Soccer (Nuryono Satya Widodo) 639 to perform localization based on the lines of the field, it needs a vision system that is able to find the lines. Figure 1. Humanoid robot soccer field (KidSize) Tabel 2. Humanoid robot soccer field dimension (KidSize) Label Name Dimension (mm) A Field length 600
One of the subcategory of RoboCup aims soccer that all the teams use humanoid robot NAO, that operates fully automatically, e.g. motion, vision, behaviour, self-localization etc, during competition of RobotCup. 1.1 Motivation. Localization technologies as a comprehensive existence saturates our daily life.
Existing localization methods. In a robot soccer game, the robots are required to localize themselves based on the information obtained from the environment. Robot self-localization techniques have been developed for a long time. Some obtain the current location from an odometer [30, 31], and some receive the current location from GPS satellites . Among these, the most developed localization algorithms estimate location based on a real-world map with the help of a distance sensor, like a ...
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when the soccer robot is performing image processing, can also very e ectively speed up the parameter setup procedure of the robot vision system. Hence, when the robot is moving in the soccer eld, it can nish target object detection and self-localization in real time. In
The intersection Landmark Based Global Self-localization of Mobile Soccer Robots 845 of such thickened circles/rings will determine the uncertainty in robot position when two or more landmarks are used. In addition to measurement errors there could be error in landmark identi- ﬁcation and matching with the world map.
Deng et al. proposed a new soccer robot self-localization method based on EKF that uses the vision information, odometer and camera, which matches the features in the image in the field. The KF and its variants integrate uncertainty into computations by making the assumption of Gaussian distributions to represent all densities of the robot positions.
A robot soccer game played by small, autonomous robots is the test-bed for this work. Constraints such as small size of the robot and the dynamic nature of the environment has to be taken into account while developing any solution for the localization.
This paper reviews three approaches to vision-based self-localization used in the RoboCup middle-size league competition and describes the results they achieve in the robot soccer environment for ...
We propose a self-localization method that uses the information on the white lines of the soccer field to recognize the robot position by optimizing the fitness function using a genetic algorithm. Through experiments we verify the robustness of the proposed method against noise and moving speed.