VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals

@article{Min2020VOLDORVO,
  title={VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals},
  author={Zhixiang Min and Yiding Yang and Enrique Dunn},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={4897-4908}
}
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Contrary to traditional methods assuming Gaussian-distributed observation errors, we supervise our inference framework under an (empirically validated) adaptive log… Expand
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