Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
This addresses robotic perception needs for efficient yet reliable 3D semantic mapping, though it appears incremental as it builds on existing methods with specific improvements.
The paper tackles real-time 3D semantic mapping by introducing a Convolutional Bayesian Kernel Inference (ConvBKI) layer that performs explicit Bayesian inference within a convolution layer, achieving improved latency with comparable semantic label inference results on the KITTI dataset.
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.