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Abstract-Self-localization is a major research task in mobile robotics for several years. Efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. It enables robots to localize themselves in real-time and to recover from localization errors. However, even those versions of MCL using an adaptive number of samples need at least a minimum in the order of 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL based on images from an omnidirectional camera system. The approach uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. We show that the method enters this efficient tracking mode after a few cycles and remains there using only a single sample for more than 90% of the cycles. Nevertheless, it is still able to cope with the kidnapped robot problem.

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Recently, efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. However, standard MCL algorithms need at least 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL that uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. Experiments show that the method remains in this efficient single sample tracking mode for more than 90% of the cycles.

A common practical problem in mobile robotics is the task to calibrate the robot's sensors. Although, the general mapping of the sensor data to robot-centered world coordinates is given by the hardware configuration, the parameters of this mapping vary even between robots with the same configuration. In the RoboCup domain, these parameters can change drastically after transport or physical contact during game play. It is therefore necessary to recalibrate the robots for their next assignment within a few minutes, not only in order to fulfill the future regulatory requirements of the RoboCup organization committee to keep the setup time as low as possible. As camera systems, especially omni-directional systems, are currently the most important sensors in RoboCup, a reliable and fast calibration method for the mapping of image to world coordinates is necessary. Since the RoboCup environment, i.e. the soccer field, has known dimensions and is also static, automatic calibration using the features and landmarks of the soccer field is possible if the robot is given an image from a known pose. In this paper, an efficient evolutionary approach to automatic camera calibration is presented, which is independent of the hardware configuration. It only requires a quality function for the parameter settings, which allows lazy evaluation. To meet the time constraints given for this real-world optimization problem, a novel mutation operator is introduced to enhance the performance of the evolutionary algorithm. It samples a number of alternative solutions using a high rate of lazy evaluation, before deciding on the true mutative change applied on the given individual. This new mutation operator proves to be fast and most reliable on the camera to world calibration problem.