Search Results for author:
Found 9 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.
Opportunities and Challenges to Integrate Artificial Intelligence into Manufacturing Systems: Thoughts from a Panel Discussion
Three overarching themes emerged from the panel discussion.
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.
Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification
In this work, we address point detection and time series classification for machine-part interactions with a deep Convolutional Neural Network (CNN) based framework.
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
In this work, we develop an iterative approach for repetitive precision motion control problems where the objective is to follow a reference geometry with minimal tracking error.
Iterative learning control with discrete-time nonlinear nonminimum phase models via stable inversion
Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models.
Receding Horizon Iterative Learning Control for Continuously Operated Systems
This paper presents an iterative learning control (ILC) scheme for continuously operated repetitive systems for which no initial condition reset exists.
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.
