3-D articulated pose tracking for untethered diectic reference

This article proposed an extended state observer (ESO)–based output feedback control scheme for rigid spacecraft pose tracking without velocity feedback, which accounts for inertial uncertainties, external disturbances, and control input constraints. In this research, the 6-DOF tracking error dynamics is described by the exponential coordinates on SE(3). A novel continuous finite-time ESO is proposed to estimate the velocity information and the compound disturbance, and the estimations are utilized in the control law design. The ESO ensures a finite-time uniform ultimately bounded stability of the observation states, which is proved utilizing the homogeneity method. A non-singular finite-time terminal sliding mode controller based on super-twisting technology is proposed, which would drive spacecraft tracking the desired states. The other two observer-based controllers are also proposed for comparison. The superiorities of the proposed control scheme are demonstrated by theory analyses and numerical simulations.

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se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9341314 ◽

2020 ◽

Author(s):

Bowen Wen ◽

Chaitanya Mitash ◽

Baozhang Ren ◽

Kostas E. Bekris

Keyword(s):

Data Driven ◽

Pose Tracking

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PoseRBPF: A Rao–Blackwellized Particle Filter for 6-D Object Pose Tracking

IEEE Transactions on Robotics ◽

10.1109/tro.2021.3056043 ◽

2021 ◽

pp. 1-15

Author(s):

Xinke Deng ◽

Arsalan Mousavian ◽

Yu Xiang ◽

Fei Xia ◽

Timothy Bretl ◽

...

Keyword(s):

Particle Filter ◽

Pose Tracking

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A Simple Baseline for Pose Tracking in Videos of Crowed Scenes

Proceedings of the 28th ACM International Conference on Multimedia ◽

10.1145/3394171.3416300 ◽

2020 ◽

Author(s):

Li Yuan ◽

Shuning Chang ◽

Ziyuan Huang ◽

Yichen Zhou ◽

Yupeng Chen ◽

...

Keyword(s):

Pose Tracking

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A Product Pose Tracking Paradigm Based on Deep Points Detection

Machines ◽

10.3390/machines9060112 ◽

2021 ◽

Vol 9 (6) ◽

pp. 112

Author(s):

Loukas Bampis ◽

Spyridon G. Mouroutsos ◽

Antonios Gasteratos

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Visible Spectrum ◽

The Body ◽

Autonomous Control ◽

Pose Tracking ◽

Computer Aided ◽

Manufacturing Procedure ◽

Infrared Cameras ◽

Representative Points

The paper at hand presents a novel and versatile method for tracking the pose of varying products during their manufacturing procedure. By using modern Deep Neural Network techniques based on Attention models, the most representative points to track an object can be automatically identified using its drawing. Then, during manufacturing, the body of the product is processed with Aluminum Oxide on those points, which is unobtrusive in the visible spectrum, but easily distinguishable from infrared cameras. Our proposal allows for the inclusion of Artificial Intelligence in Computer-Aided Manufacturing to assist the autonomous control of robotic handlers.

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