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.

翻译：机器人感知目前正处于现代方法与经典方法的交叉路口：现代方法在高效隐空间中进行运算，而经典方法具有数学基础并能提供可解释、可信赖的结果。本文提出了一种卷积贝叶斯核推断（ConvBKI）层，该层在深度可分离卷积层内学习执行显式贝叶斯推断，以在保持可靠性的同时最大化效率。我们将该层应用于实时三维语义建图任务，通过学习激光雷达传感器信息的语义-几何概率分布，将语义预测融入全局地图中。我们在KITTI数据集上将该网络与最先进的语义建图算法进行对比，结果表明在语义标签推断性能相当的情况下，延迟得到了改善。