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This comprehensive review article surveys recent advancements in deep learning applications and methods for autonomous navigation. We …

• End-to-end learning framework for real-time inertial attitude estimation. Generalized across various sampling rates. RNN-CNN networks employed to learn motion characteristics, noise, and bias. Proposed approach outperforms traditional algorithms and other deepOutperforms traditional algorithms in terms of accuracy up to 40 Evaluated using seven datasets, totaling 120 h and 200 kilometers of IMU measurements.

Abstract Introduction Problem definition Background Attitude Attitude Determination from Inertial Sensors Methodology Deep Learning Model Loss Function Experiment Dataset RepoIMU T-stick RepoIMU T-pendulum Sassari OxIOD MAV Dataset EuRoC MAV TUM-VI KITTI RIDI RoNIN BROAD Training Evaluation Abstract This article discusses the importance of accurate and precise attitude determination in navigation for air and space vehicles.

Abstract Introduction Problem Defenition Literature Review Backgroud Methodology Deep Learning Model Loss Function Experiment Dataset Abstract Despite recent advancments in Micro-Electro Mechanical Systems (MEMS) inertial and magnetic sensors, percices and accurate attitude estimation is a challenging task, especillay in the existance of magnetic distubances or high dynamic motions.

Introduction Related works Problem definition Background Attitude Attitude Determination from Inertial Sensors Methodology Deep Learning Model Loss Function Experiment Dataset Introduction Achieving accurate and precise attitude determination or estimation is needed to perform successful navigation.

Self-Localization and Odometry Table of Contents Self-Localization and Odometry Table of Contents Introduction Related Work Inertial Navigation Systems Deep Learning Approaches Background Inertial Navigation Principles \end{bmatrix} \end{bmatrix} \end{bmatrix} \end{bmatrix} 6-DoF relative pose representaion 6 DoF Inertial Odometry Neural Network Network Architecture Error Metrices Quaternion Inner Product Quaternion Multiplicative Error Quaternion Shortest Geodesic Distance Loss Function Experiment Dataset Training Evaluation Simulation Results Conclusion Introduction Self-localization is one of the main challenges in the application of autonomous systems.

Spacecraft Trajectory Optimization Introduction Prerequisites Goals Table of Contents 1. Problem Formulation 1.1 Define the Mission Parameters 1.1.1 Departure and Arrival Locations 1.1.2 Departure and Arrival Times 1.1.3 Spacecraft Mass and Constraints 1.