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A real-time semantic 3D occupancy mapping framework is proposed in this paper. The framework is based on the Bayesian kernel inference strategy. 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, due to the coverage completeness of the proposed algorithm. Experiments on real-world point cloud scan datasets are presented.

Real-time Semantic 3D Dense Occupancy Mapping with Efficient Free Space Representations