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.

本文提出了一种基于贝叶斯方法的连续三维语义占用地图构建方法，通过对贝叶斯内核推理模型的推广，从二进制问题到多类问题，实现了统一的概率模型和采用贝叶斯空间内核推理来提高地图构建的性能，同时在多序列数据上得到了较好的效果。