  In densely populated environments, socially compliant navigation is critical
for autonomous robots as driving close to people is unavoidable. This manner of
social navigation is challenging given the constraints of human comfort and
social rules. Traditional methods based on hand-craft cost functions to achieve
this task have difficulties to operate in the complex real world. Other
learning-based approaches fail to address the naturalness aspect from the
perspective of collective formation behaviors. We present an autonomous
navigation system capable of operating in dense crowds and utilizing
information of social groups. The underlying system incorporates a deep neural
network to track social groups and join the flow of a social group in
facilitating the navigation. A collision avoidance layer in the system further
ensures navigation safety. In experiments, our method generates socially
compliant behaviors as state-of-the-art methods. More importantly, the system
is capable of navigating safely in a densely populated area (10+ people in a
10m x 20m area) following crowd flows to reach the goal.
