  We present DenseCAvoid, a novel navigation algorithm for navigating a robot
through dense crowds and avoiding collisions by anticipating pedestrian
behaviors. Our formulation uses visual sensors and a pedestrian trajectory
prediction algorithm to track pedestrians in a set of input frames and provide
bounding boxes that extrapolate the pedestrian positions in a future time. Our
hybrid approach combines this trajectory prediction with a Deep Reinforcement
Learning-based collision avoidance method to train a policy to generate
smoother, safer, and more robust trajectories during run-time. We train our
policy in realistic 3-D simulations of static and dynamic scenarios with
multiple pedestrians. In practice, our hybrid approach generalizes well to
unseen, real-world scenarios and can navigate a robot through dense crowds
(~1-2 humans per square meter) in indoor scenarios, including narrow corridors
and lobbies. As compared to cases where prediction was not used, we observe
that our method reduces the occurrence of the robot freezing in a crowd by up
to 48%, and performs comparably with respect to trajectory lengths and mean
arrival times to goal.
