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  • Simultaneous Localization and Mapping in Three-dimensional Space using Parallel Computations and Based on Data Obtained from a Video Camera

Simultaneous Localization and Mapping in Three-dimensional Space using Parallel Computations and Based on Data Obtained from a Video Camera

Student: Smolnikova Polina

Supervisor: Valery A. Kalyagin

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Simultaneous localization and mapping algorithms have been widely used in robotics in the last few years. These algorithms use different sources like lasers, sensors, cameras as input information to generate 3D maps and find camera location. This paper presents open-source visual SLAM based on ORB SLAM system ideas. The model extracts key points from the image and uses this information for 3 parallel tasks - tracking, local mapping and loop closing. The system input requires image sequences obtained from a calibrated monocular camera. The OpenCV library is used as a basement for our implementation. It provides a convenient interface for users, the ability to commit preferred changes to the code and has a user-friendly environment for further functionality development. Besides, the present work highlights still unresolved localization and mapping problems. Thus, it aims to attract researchers to this task providing efficient functionality. The system was evaluated on the public KITTY visual odometry dataset, including 11 image sequences with ground truth camera trajectories. Keywords: Localization, Mapping, Monocular Vision, SLAM, OpenCV

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