Loss it right: Euclidean and Riemannian Metrics in Learning-based Visual
  Odometry
Olaya Álvarez-Tuñón Yury Brodskiy Erdal Kayacan
This paper overviews different pose representations and metric functions in
visual odometry (VO) networks. The performance of VO networks heavily relies on
how their architecture encodes the information. The choice of pose
representation and loss function significantly impacts network convergence and
generalization. We investigate these factors in the VO network DeepVO by
implementing loss functions based on Euler, quaternion, and chordal distance
and analyzing their influence on performance. The results of this study provide
insights into how loss functions affect the designing of efficient and accurate
VO networks for camera motion estimation. The experiments illustrate that a
distance that complies with the mathematical requirements of a metric, such as
the chordal distance, provides better generalization and faster convergence.
The code for the experiments can be found at
https://github.com/remaro-network/Loss_VO_right