Attitude Estimation

• 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.

Table of Contents Table of Contents Introduction Attitude and Attitude Terminology References Introduction Attitude determination and control play a vital role in Aerospace engineering. Most aerial or space vehicles have subsystem(s) that must be pointed to a specific direction, known as pointing modes, e.