OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors
This work addresses efficient and safe robot navigation in crowded environments for robotics applications, representing an incremental improvement over existing velocity-obstacle methods.
The paper tackles robot navigation among pedestrians by developing a modified velocity-obstacle algorithm that uses optical flow and sensor fusion for perception, resulting in improved navigation time and collision avoidance success rates compared to prior methods, with real-time performance demonstrated on a Turtlebot.
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/