  Robotic perception is currently at a cross-roads between modern methods,
which operate in an efficient latent space, and classical methods, which are
mathematically founded and provide interpretable, trustworthy results. In this
paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer
which learns to perform explicit Bayesian inference within a depthwise
separable convolution layer to maximize efficency while maintaining reliability
simultaneously. We apply our layer to the task of real-time 3D semantic
mapping, where we learn semantic-geometric probability distributions for LiDAR
sensor information and incorporate semantic predictions into a global map. We
evaluate our network against state-of-the-art semantic mapping algorithms on
the KITTI data set, demonstrating improved latency with comparable semantic
label inference results.
