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.
@article{arxiv.2209.10663,
title = {Convolutional Bayesian Kernel Inference for 3D Semantic Mapping},
author = {Joey Wilson and Yuewei Fu and Arthur Zhang and Jingyu Song and Andrew Capodieci and Paramsothy Jayakumar and Kira Barton and Maani Ghaffari},
journal= {arXiv preprint arXiv:2209.10663},
year = {2023}
}