ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty

• URL: http://arxiv.org/abs/2310.16020v3
• Date: Fri, 01 Nov 2024 13:59:05 GMT
• Title: ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
• Authors: Joey Wilson, Yuewei Fu, Joshua Friesen, Parker Ewen, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari,
• Abstract summary: We develop a modular neural network for real-time colorblack(> 10 Hz) semantic mapping in uncertain environments. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks.
• Score: 7.537718151195062
• License:
• Abstract: In this paper, we develop a modular neural network for real-time {\color{black}(> 10 Hz)} semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel Convolutional Bayesian Kernel Inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a Robot Operating System (ROS) package of ConvBKI and test it on real-world perceptually challenging off-road driving data.

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