• Corpus ID: 244477984

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

  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},
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… 

A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation

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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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GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

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D3VO tightly incorporates the predicted depth, pose and uncertainty into a direct visual odometry method to boost both the front-end tracking as well as the back-end non-linear optimization.

Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias, and Rolling Shutter Effect

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