  This paper develops a Bayesian continuous 3D semantic occupancy map from
noisy point clouds by generalizing the Bayesian kernel inference model for
building occupancy maps, a binary problem, to semantic maps, a multi-class
problem. The proposed method provides a unified probabilistic model for both
occupancy and semantic probabilities and nicely reverts to the original
occupancy mapping framework when only one occupied class exists in obtained
measurements. The Bayesian spatial kernel inference relaxes the independent
grid assumption and brings smoothness and continuity to the map inference,
enabling to exploit local correlations present in the environment and
increasing the performance. The accompanying software uses multi-threading and
vectorization, and runs at about 2 Hz on a laptop CPU. Evaluations using
multiple sequences of stereo camera and LiDAR datasets show that the proposed
method consistently outperforms current baselines. We also present a
qualitative evaluation using data collected with a bipedal robot platform on
the University of Michigan - North Campus.
