This paper deals with design of hybrid software and hardware sensor, which can be used for mobile robots visual odometry task. The paper is focused to sensing raw data only. This approach combines onboard hardware sensorsaccelerometer, gyroscope, magnetometer and camera with software module, which is mainly based on computer vision. Output from vision system is relative change of position and rotation of mobile robot. The position change is calculated for two axes based on vision data and the relative rotation is based on data fusion of three onboard sensors.
This paper deals with design of visual SLAM method, which is based on phase correlation and particle filters. This method can be used for localization of autonomous mobile robots inside of buildings. The method contains two parts. The first one is mapping of environment, where the mobile robot operates. For this purpose was used phase correlation and images stitching method. The second one is localization problem, which was solved by particle filters, where a particles weights re-sampling was realized by phase correlation image processing method as well. Localization uses the map, which was created by phase correlation and stitching method.
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