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

Autor: Wilson, Joey, Fu, Yuewei, Friesen, Joshua, Ewen, Parker, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, Ghaffari, Maani

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

Externí odkaz: http://arxiv.org/abs/2310.16020

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Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Autor: Wilson, Joey, Fu, Yuewei, Zhang, Arthur, Song, Jingyu, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, Ghaffari, Maani

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

Externí odkaz: http://arxiv.org/abs/2209.10663

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MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments

Autor: Wilson, Joey, Song, Jingyu, Fu, Yuewei, Zhang, Arthur, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, Ghaffari, Maani

This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises gene

Externí odkaz: http://arxiv.org/abs/2203.07060

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Mobile 3D Printing Robot Simulation with Viscoelastic Fluids

Autor: Berdica, Uljad, Fu, Yuewei, Liu, Yuchen, Angelidis, Emmanouil, Feng, Chen

The system design and algorithm development of mobile 3D printing robots need a realistic simulation. They require a mobile robot simulation platform to interoperate with a physics-based material simulation for handling interactions between the time-

Externí odkaz: http://arxiv.org/abs/2110.04412

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