Distance measurement plays a key role in many fields of science and technology, including robotics, civil engineering, and navigation systems. This paper focuses on analyzing the precision of a measurement system using stereo camera distance measurement technology in the context of measuring two objects of different sizes. The first part of the paper presents key information about stereoscopy, followed by a discussion of the process of building a measuring station. The Mask R-CNN algorithm, which is a deep learning model that combines object detection and instance segmentation, was used to identify objects in the images. In the following section, the calibration process of the system and the distance calculation method are presented. The purpose of the study was to determine the precision of the measurement system and to identify the distance ranges where the measurements are most precise. Measurements were made in the range of 20 to 70 cm. The system demonstrated a relative error of 0.95% for larger objects and 1.46% for smaller objects at optimal distances. A detailed analysis showed that for larger objects, the system exhibited higher precision over a wider range of distances, while for smaller objects, the highest accuracy was achieved over a more limited range. These results provide valuable information on the capabilities and limitations of the measurement system used, while pointing out directions for its further optimization.

This paper presents how forces are perceived in a racing simulator based on a Stewart Platform. By retrieving calculated forces in a racing game by its physics engine and comparing them to real-life measurements during the platforms motions it is possible to evaluate the platforms immersiveness....