Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Abstract-- 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 Li-DAR sensor information and incorporate semantic predictions into a global map. The constructed semantic volumes are convolved with a depthwise filter to perform a real-time Bayesian update on a semantic 3D map. Robust world models are essential for safe and reliable the structured geometric representations of earlier, probabilistic autonomous robots.