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The localization of mobile robots has become one of the most frequently encountered problems in indoor and outdoor environments. There are some suggestions for solving this problem. Mobile robot positions often can not be directly measured. Therefore, the position measurements of mobile robots has to be correctly determined by combining data received from multiple sensors. Multiple sensor approaches are particularly used in different areas such as automatic cranes, autonomous vehicles, wheelchairs and autonomous mobile driving platforms. Odometry sensors which provide data about the position and behavior of a robot are usually used in positioning of mobile robots. However, these data are not precise enough and noisy. Therefore, the employment of odometry sensors alone is not appropriate. The position and behavior of a mobile robot can be predicted via the multi-sensor approach using data obtained from such equipment as encoders, gyroscopes, GPS receivers, laser range finder, magnetic sensors, kinect sensors, etc. Researchers have used many of these sensors to perform mapping and localization operations [1–3]. Kalman filters and variants are generally used to improve the prediction results of mobile robots indoor/outdoor motion. Fernández et al. presented a new approach for positioning and guiding of mobile robots in indoor environments. They used an odometric system in conjunction with a

∗ corresponding author; e-mail: zbatik@ sakarya. edu. tr

Kalman filter for the positioning of a robot. The cameras were also used to receive the visual information [4]. Santana et al. applied the Kalman filter to the data obtained from …