This paper implements a monocular vision simultaneous localization and mapping (SLAM) simulation system and describes its design process. The system has good working performance, and its SLAM algorithm is highly scalable, which can accurately approximate the real monocular vision SLAM process. With the goal of facilitating the research of SLAM algorithms, it provides a large number of functions to assist in recording, observing and analyzing experimental results, which is helpful for the reproduction of experiments and the comparative study of the effects of different algorithms. Keywords: monocular vision sensor; extended Kalman filter; simultaneous localization and mapping; mobile robot
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