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Deep Learning for UAS Position Estimation by Trung Con is a document available to read on EtoBox.

What is Deep Learning for UAS Position Estimation about?

The document presents HVIOnet, a deep learning-based hybrid visual-inertial odometry approach designed for unmanned aerial systems (UAS) position estimation. It utilizes a Convolutional Neural Network (CNN) for visual feature extraction and a Bidirectional Long Short Term Memory (BiLSTM) network for processing inertial data, achieving successful position estimations with a Mean Square Error of 0.167 in tests using the EuRoC dataset. The proposed method demonstrates improved accuracy and robustness in UAS lo

Author

Trung Con

Language

EN