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Found 5 papers, 3 papers with code
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
In this paper, we develop a modular neural network for real-time semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer.
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
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.
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes.
Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference
This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model.
An Active Learning Framework for Constructing High-fidelity Mobility Maps
A mobility map, which provides maximum achievable speed on a given terrain, is essential for path planning of autonomous ground vehicles in off-road settings.
