• Corpus ID: 244477984

Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle Adjustment

@article{Cao2021RobustVO,
  title={Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle Adjustment},
  author={Yijun Cao and Xian-Shi Zhang and Fuya Luo and Peng Peng and Yongjie Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.11141}
}
In this paper, an essential problem of robust visual odometry (VO) is approached by incorporating geometry-based methods into deep-learning architecture in a self-supervised manner. Generally, pure geometry-based algorithms are not as robust as deep learning in feature-point extraction and matching, but perform well in ego-motion estimation because of their well-established geometric theory. In this work, a novel optical flow network (PANet) built on a position-aware mechanism is proposed first… 
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2 Citations

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Unsupervised Vision and Vision-motion Calibration Strategies for PointGoal Navigation in Indoor Environment

Novel vision and vision- motion calibration strategies to train visual and motion path integration in unsupervised manner and outperforms the partially supervised learning algorithms on the popular Gibson dataset are proposed.

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