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Special Orthogonal Group SO(3), Euler Angles, Angle-axis, Rodriguez Vector and Unit-Quaternion: Overview, Mapping and Challenges

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arxiv 1909.06669 v5 pith:KNTUAXN6 submitted 2019-09-14 math.OC cs.SYeess.SY

classification math.OCcs.SYeess.SY
keywords attitudeparameterizationangle-axisangleseulergrouprodriguezunit-quaternion
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The attitude of a rigid-body in the three dimensional space has a unique and global definition on the Special Orthogonal Group SO (3). This paper gives an overview of the rotation matrix, attitude kinematics and parameterization. The four most frequently used methods of attitude representations are discussed with detailed derivations, namely Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion. The mapping from one representation to others including SO (3) is given. Also, important results which could be useful for the process of filter and/or control design are given. The main weaknesses of attitude parameterization using Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion are illustrated. Keywords: Special Orthogonal Group 3, Euler angles, Angle-axis, Rodriguez Vector, Unit-quaternion, SO(3), Mapping, Parameterization, Attitude, Control, Filter, Observer, Estimator, Rotation, Rotational matrix, Transformation matrix, Orientation, Transformation, Roll, Pitch, Yaw, Quad-rotor, Unmanned aerial vehicle, Robot, spacecraft, satellite, UAV, Underwater vehicle, autonomous, system, Pose, literature review, survey, overview, comparison, comparative study, body frame, identity, origin, dynamics, kinematics, Lie group, inertial frame, zero, filter, control, estimate, observation, measurement, 3D, three dimensional space, advantage, disadvantage.

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Cited by 2 Pith papers

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    cs.RO 2026-07 reject novelty 6.0 of 10

    A sampling-based planner that biases samples toward near-contact configurations solves previously unsolved tight assembly puzzles and is claimed to be probabilistically complete.

  2. DeepUKF-VIN: Adaptively-tuned Deep Unscented Kalman Filter for 3D Visual-Inertial Navigation based on IMU-Vision-Net

    cs.RO 2025-02 reject novelty 4.0 of 10

    A deep learning module that tunes UKF noise covariances for visual-inertial navigation is proposed, but its claimed consistent advantage over a standard UKF is not supported by the paper's own tables.

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