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Found 8 papers, 2 papers with code
GPU-Accelerated 3D Polygon Visibility Volumes for Synergistic Perception and Navigation
UAV missions often require specific geometric constraints to be satisfied between ground locations and the vehicle location.
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
OOD data consists of test data that is significantly different from the model's training data.
Photometric Correction for Infrared Sensors
To explore the possibility of 3D reconstruction via SfM from infrared images, this article proposes a photometric correction model for infrared sensors based on temperature constancy.
DOMINO: Domain-aware Loss for Deep Learning Calibration
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered.
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation.
Information-Aware Guidance for Magnetic Anomaly based Navigation
In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance.
A summary on the UD Kalman Filter
This document contains a concise and unified reference for one of the existing mechanizations of the UD Kalman filter.
ROS georegistration: Aerial Multi-spectral Image Simulator for the Robot Operating System
The novelty of this work is threefold: (1) this is the first system to link the massive multi-spectral imaging database of Google's Earth Engine to the Gazebo simulator, (2) this is the first example of a system that can simulate geospatially and radiometrically accurate imagery from multiple sensor views of the same terrain region, and (3) integration with other UAS tools creates a new holistic UAS simulation environment to support UAS system and subsystem development where real-world testing would generally be prohibitive.
