  We present a modified velocity-obstacle (VO) algorithm that uses
probabilistic partial observations of the environment to compute velocities and
navigate a robot to a target. Our system uses commodity visual sensors,
including a mono-camera and a 2D Lidar, to explicitly predict the velocities
and positions of surrounding obstacles through optical flow estimation, object
detection, and sensor fusion. A key aspect of our work is coupling the
perception (OF: optical flow) and planning (VO) components for reliable
navigation. Overall, our OF-VO algorithm using learning-based perception and
model-based planning methods offers better performance than prior algorithms in
terms of navigation time and success rate of collision avoidance. Our method
also provides bounds on the probabilistic collision avoidance algorithm. We
highlight the realtime performance of OF-VO on a Turtlebot navigating among
pedestrians in both simulated and real-world scenes. A demo video is available
at https://gamma.umd.edu/ofvo/
