  Humans can robustly follow a visual trajectory defined by a sequence of
images (i.e. a video) regardless of substantial changes in the environment or
the presence of obstacles. We aim at endowing similar visual navigation
capabilities to mobile robots solely equipped with a RGB fisheye camera. We
propose a novel probabilistic visual navigation system that learns to follow a
sequence of images with bidirectional visual predictions conditioned on
possible navigation velocities. By predicting bidirectionally (from start
towards goal and vice versa) our method extends its predictive horizon enabling
the robot to go around unseen large obstacles that are not visible in the video
trajectory. Learning how to react to obstacles and potential risks in the
visual field is achieved by imitating human teleoperators. Since the human
teleoperation commands are diverse, we propose a probabilistic representation
of trajectories that we can sample to find the safest path. Integrated into our
navigation system, we present a novel localization approach that infers the
current location of the robot based on the virtual predicted trajectories
required to reach different images in the visual trajectory. We evaluate our
navigation system quantitatively and qualitatively in multiple simulated and
real environments and compare to state-of-the-art baselines.Our approach
outperforms the most recent visual navigation methods with a large margin with
regard to goal arrival rate, subgoal coverage rate, and success weighted by
path length (SPL). Our method also generalizes to new robot embodiments never
used during training.
