  Safe navigation in unknown environments stands as a significant challenge in
the field of robotics. Control Barrier Function (CBF) is a strong mathematical
tool to guarantee safety requirements. However, a common assumption in many
works is that the CBF is already known and obstacles have predefined shapes. In
this letter, we present a novel method called Occupancy Grid Map-based Control
Barrier Function (OGM-CBF), which defines Control Barrier Function based on
Occupancy Grid Maps. This enables generalization to unknown environments while
generating online local or global maps of the environment using onboard
perception sensors such as LiDAR or camera. With this method, the system
guarantees safety via a single, continuously differentiable CBF per time step,
which can be represented as one constraint in the CBF-QP optimization
formulation while having an arbitrary number of obstacles with unknown shapes
in the environment. This enables practical real-time implementation of CBF in
both unknown and known environments. The efficacy of OGM-CBF is demonstrated in
the safe control of an autonomous car in the CARLA simulator and a real-world
industrial mobile robot.
