Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
An Automated Refereeing and Analysis Tool for the Four-Legged League
RoboCup 2006: Robot Soccer World Cup X
Orientation Extraction and Identification of the Opponent Robots in RoboCup Small-Size League
RoboCup 2006: Robot Soccer World Cup X
A Robot Referee for Robot Soccer
RoboCup 2008: Robot Soccer World Cup XII
Interpolation methods for global vision systems
RoboCup 2004
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State estimation is of crucial importance to mobile robotics since it determines in a great measure its ability to model the world from noisy observations. In order to quantitatively evaluate state-estimation methods, the availability of ground-truth data is essential since it provides a target that the result of the state-estimation methods should approximate. Most of the reported ground-truth systems require a complex assembly which limit their applicability and make their set-up long and complicated. Furthermore, they often require a long calibration procedure. Additionally, they do not present measures of their accuracy. This paper proposes a portable laser-based ground-truth system. The proposed system can be easily ported from one environment to other and requires almost no calibration. Quantitative results are presented with the purpose of encouraging future comparisons among different groundtruth systems. The presented method has shown to be accurate enough to evaluate state-estimation methods and works in real time.