  We present a method to automatically learn to segment dynamic objects using
SLAM outliers. It requires only one monocular sequence per dynamic object for
training and consists in localizing dynamic objects using SLAM outliers,
creating their masks, and using these masks to train a semantic segmentation
network. We integrate the trained network in ORB-SLAM 2 and LDSO. At runtime we
remove features on dynamic objects, making the SLAM unaffected by them. We also
propose a new stereo dataset and new metrics to evaluate SLAM robustness. Our
dataset includes consensus inversions, i.e., situations where the SLAM uses
more features on dynamic objects that on the static background. Consensus
inversions are challenging for SLAM as they may cause major SLAM failures. Our
approach performs better than the State-of-the-Art on the TUM RGB-D dataset in
monocular mode and on our dataset in both monocular and stereo modes.
