Motion Consistency Loss for Monocular Visual Odometry with
  Attention-Based Deep Learning
André O. Françani Marcos R. O. A. Maximo
Deep learning algorithms have driven expressive progress in many complex
tasks. The loss function is a core component of deep learning techniques,
guiding the learning process of neural networks. This paper contributes by
introducing a consistency loss for visual odometry with deep learning-based
approaches. The motion consistency loss explores repeated motions that appear
in consecutive overlapped video clips. Experimental results show that our
approach increased the performance of a model on the KITTI odometry benchmark.