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MonoDepth-vSLAM: A Visual EKF-SLAM using Optical Flow and Monocular Depth Estimation

Dey, Rohit
http://orcid.org/0000-0001-5786-0608
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Over the years, fully autonomous vehicles(AV) have made significant progress towards being employed in real-life scenarios, although they still lack enough sophistication to drive through a completely unknown environment. Real-life scenarios such as during search and rescue operations, infrastructure inspection, or even indoor navigation demands AVs to work in an unfamiliar GPS-denied environment. This kind of situation is usually dealt as SLAM problems and solved using sensors like LiDAR, Stereo, and Monocular cameras. Even though approaches using LiDAR and Stereo cameras have shown some promising results, they require significant amounts of processing power and occupy considerable space, two factors that are inherently limited with an in-vehicle computer system. An alternative is to use a monocular camera that is both easy to install and light on processing. However, they suffer from an absence of depth data to perceive the 3D world around it to successfully perform SLAM. This research addresses the problem by proposing ``MonoDepth-vSLAM" - a novel robust visual SLAM algorithm using a single monocular camera. The algorithm uses Extended Kalman Filter with an optical flow-based model for real-time pose estimation and feature tracking, assisted by monocular depth estimation obtained from self-supervised neural networks. The classical EKF approach provides a lightweight method for tracking vehicle states with uncertainty bounds, whereas the depth estimation network mitigates the absence of depth information required to perform monocular SLAM successfully. The proposed algorithm is designed to work in real-time and has been tested and analyzed in different scenarios.
Manish Kumar, Ph.D. (Committee Chair)
Rajnikant Sharma, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
75 p.
Robots
Visual SLAM; Optical Flow; EKF; Monocular Camera; Depth Estimation Neural Network; CARLA

Recommended Citations

Citations

  • Dey, R. (2021). MonoDepth-vSLAM: A Visual EKF-SLAM using Optical Flow and Monocular Depth Estimation [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079

    APA Style (7th edition)

  • Dey, Rohit. MonoDepth-vSLAM: A Visual EKF-SLAM using Optical Flow and Monocular Depth Estimation. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079.

    MLA Style (8th edition)

  • Dey, Rohit. "MonoDepth-vSLAM: A Visual EKF-SLAM using Optical Flow and Monocular Depth Estimation." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079

    Chicago Manual of Style (17th edition)

ucin1627666226301079
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© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.