Deep learning feature-based visual SLAM
Visual SLAM is often used in autonomous agents for localization and mapping. A key element of visual SLAM is the feature detector-descriptor used to extract tracking keypoints from images of the environment. Classical visual SLAM algorithms rely on hand-crafted methods, which have low computation...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/166008 |
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| 總結: | Visual SLAM is often used in autonomous agents for localization and mapping. A
key element of visual SLAM is the feature detector-descriptor used to extract tracking
keypoints from images of the environment. Classical visual SLAM algorithms rely on
hand-crafted methods, which have low computational complexity but suffer from poor
accuracy. Deep-learned feature detector descriptors have the potential to improve feature
accuracy and robustness and thus tracking performance.
This paper aims to compare the advantages and disadvantages of classical and deeplearning
feature detector-descriptors for use in mono-SLAM through a review of literature
and comparing the experimental performance of ORB-SLAM2 and SuperPoint-SLAM. A
broad set of experiments covering multiple conditions such as dynamic scenes, illumination
changes, and fast motion have been performed to determine baseline performance as well
as reveal which conditions favour each detector-descriptor type. |
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