Papers › Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
Joey Wilson, Yuewei Fu, Arthur Zhang, Jingyu Song, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari
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.
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