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- W4296932837 abstract "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." @default.
- W4296932837 created "2022-09-24" @default.
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- W4296932837 date "2022-09-21" @default.
- W4296932837 modified "2023-10-18" @default.
- W4296932837 title "Convolutional Bayesian Kernel Inference for 3D Semantic Mapping" @default.
- W4296932837 doi "https://doi.org/10.48550/arxiv.2209.10663" @default.
- W4296932837 hasPublicationYear "2022" @default.
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