In this article, we develop a modular neural network for real-time (>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 3-D 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 package of ConvBKI and test it on real-world perceptually challenging off-road driving data.

中文翻译：

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ConvBKI：具有可量化不确定性的实时概率语义映射网络


在本文中，我们开发了一个模块化神经网络，用于在不确定环境中进行实时 （>10 Hz） 语义映射，它显式更新了神经网络层内每个体素的概率分布。我们的方法将经典概率算法的可靠性与现代神经网络的性能和效率相结合。尽管机器人感知通常分为现代可微分方法和经典显式方法，但两者的结合对于实时和可信的性能是必要的。我们引入了一种新的卷积贝叶斯核推理 （ConvBKI） 层，该层通过利用共轭先验，通过深度卷积层将语义分割预测在线整合到 3-D 地图中。我们将 ConvBKI 与最先进的深度学习方法和概率映射算法进行比较，以评估可靠性和性能。我们还创建了一个 ConvBKI 机器人操作系统包，并在现实世界中具有感知挑战性的越野驾驶数据上对其进行了测试。