Over the last years, the use of multiple cameras is becoming more and more popular in today’s computer vision systems. Such approach is widely used in many applications, such as navigation of autonomous mobile robots, video surveillance, the movie industry, augmented reality or people tracking and identification systems. Surprisingly, little attention is paid in the literature to the practical calibration procedures that can be employed to map space between various vision systems. Therefore, in this paper a novel approach that allows to map space between cameras with different coordinate systems: Cartesian and polar is presented. The practical problems that occurs in such scenarios are analysed and thoroughly discussed. The authors present stepby-step description of the proposed calibration procedure. A series of experiments were conducted to confirm the correctness of the presented approach and to demonstrate how to apply the developed solution in practical applications. The proposed method does not require any additional equipment beyond the standard calibration chessboard. The achieved results indicate, that for evaluated cameras configuration, the maximum mapping error for the horizontal and vertical axes does not exceed 0.6°. Obtained results are encouraging and useful for development of similar solutions.
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