KRF-SLAM: A Robust AI Slam Based On Keypoint Resampling And Fusion

Artificial Intelligence (AI) based feature extractors provide new possibility in the localization problem because of trainable characteristic. In this paper, the confidence information from AI learning process is used to further improve the accuracy. By resampling interest points based on different...

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Bibliographic Details
Main Authors: Wong, Wai Mun, Lim, Christopher, Lee, Chia-Da, Wang, Lilian, Chen, Shih-Che, Tsung, Pei-Kuei
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Computer Vision
Deep Neural Network
Head
Iron
Optimization
Robustness
Simultaneous localization and mapping
Trajectory
Online Access:Request full text
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Summary:Artificial Intelligence (AI) based feature extractors provide new possibility in the localization problem because of trainable characteristic. In this paper, the confidence information from AI learning process is used to further improve the accuracy. By resampling interest points based on different confidence thresholds, we are able to pixel-stack highlyconfident interest points to increase their bias for pose optimization. Then, the complementary descriptors are used to describe the pixel stacked interest points. As the result, the proposed Keypoint Resampling and Fusion (KRF) method improves the absolute trajectory error by 40% over state-of the-art vision SLAM algorithm on TUM Freiburg dataset. It is also more robust against tracking lost, and is compatible with existing optimizers.
ISSN:2381-8549
DOI:10.1109/ICIP40778.2020.9191192