Convolutional Bayesian Kernel Inference for 3D Semantic Mapping Article Swipe

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Inference Computer science Artificial intelligence Kernel (algebra) Bayesian inference Bayesian network Probabilistic latent semantic analysis Set (abstract data type) Bayesian probability Machine learning Mathematics Combinatorics Programming language

Joey Wilson , Yuewei Fu , Arthur Zhang , Jingyu Song , Andrew Capodieci , Paramsothy Jayakumar , Kira Barton , Maani Ghaffari ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2209.10663 · OA: W4296932837

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.