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Found 7 papers, 1 papers with code
Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning
Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience.
Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization
One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tolerance mechanisms to deal with performance degradation.
Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets
Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications.
Sparse Optical Flow-Based Line Feature Tracking
To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints; next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function; finally, connect the two tracked endpoints to generate a new line feature.
Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative Characterization of SLAM Datasets
In order to fill this void, characterization of the operating conditions of SLAM systems is essential in order to provide an environment for quantitative measurement of robustness and resilience.
Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features
Asset health monitoring continues to be of increasing importance on productivity, reliability, and cost reduction.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}.
