• Corpus ID: 244728383

Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM

@article{Loo2021OnlineMA,
  title={Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM},
  author={Shing Yan Loo and Moein Shakeri and Sai Hong Tang and Syamsiah Mashohor and Hong Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.04096}
}
The ability of accurate depth prediction by a CNN is a major challenge for its wide use in practical visual SLAM applications, such as enhanced camera tracking and dense mapping. This paper is set out to answer the following question: Can we tune a depth prediction CNN with the help of a visual SLAM algorithm even if the CNN is not trained for the current operating environment in order to benefit the SLAM performance? To this end, we propose a novel online adaptation framework consisting of two… 
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Background Citations
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3 Citations

An Overview on Visual SLAM: From Tradition to Semantic

This paper introduces the development of VSLAM technology from two aspects: traditional V SLAM and semantic VSLam combined with deep learning, and focuses on the developmentof semantic V SLam based on deep learning.

HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera

A hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature-based triangulation and depth estimation, which surpasses that of the state-of-the-art (SOTA) SLAM systems.

CoVIO: Online Continual Learning for Visual-Inertial Odometry

This work introduces CoVIO for online continual learning of visual-inertial odometry and proposes a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices.

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