ConvBKI: 不確実性を定量化できるリアルタイム確率的意味マッピングネットワーク
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
畳み込みベイズカーネル推論層を導入し、古典的な確率的手法とニューラルネットを組み合わせて、不確実な環境で10Hz以上のリアルタイム3D意味マッピングを実現した研究。
著者: Joey Wilson, Yuewei Fu, Joshua Friesen, Parker Ewen, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari
分類: cs.RO, cs.CV
原文アブストラクト
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.
