  A real-time semantic 3D occupancy mapping framework is proposed in this
paper. The mapping framework is based on the Bayesian kernel inference strategy
from the literature. Two novel free space representations are proposed to
efficiently construct training data and improve the mapping speed, which is a
major bottleneck for real-world deployments. Our method achieves real-time
mapping even on a consumer-grade CPU. Another important benefit is that our
method can handle dynamic scenarios, thanks to the coverage completeness of the
proposed algorithm. Experiments on real-world point cloud scan datasets are
presented.
