Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

• URL: http://arxiv.org/abs/2209.10663v2
• Date: Wed, 31 May 2023 19:41:32 GMT
• Title: Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
• Authors: Joey Wilson, Yuewei Fu, Arthur Zhang, Jingyu Song, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, and Maani Ghaffari
• Abstract summary: We introduce a Convolutional Bayesian Kernel Inference layer which learns to perform explicit Bayesian inference. 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.
• Score: 1.7615233156139762
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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