  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-logistic distribution model.
Moreover, the log-logistic residual model generalizes well to different
state-of-the-art optical flow methods, making our approach modular and agnostic
to the choice of optical flow estimators. Our method achieved top-ranking
results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced
implementation is inherently GPU-friendly with only linear computational and
storage growth.
