Visual SLAM for humanoid robot localization and closed-loop control

Abstract : This thesis deals with the problem of localizing and controlling humanoid robots with respect to its environment, as observed by its on-board sensors. Dense visual SLAM, consisting in the simultaneous estimation of a 3D map of the environment and of the robot localization within that maps is exploited to extend and robustify multi-contact planning and control. Establishing and exploiting robot-environment contacts allows the accomplishment of both locomotion and manipulation tasks. Uncertainties in the initial robot posture, and perturbations arising from improper contact-modelling and external causes are accounted for by observing the state of the robot and its environment. A whole-body calibration method is also proposed, so that robust knowledge of the robot's kinematic structure is known, a prerequisite to all robot-environment interaction tasks. Finally, a walking method based on model predictive control is robustified by taking into account large perturbations, and adjusting the footstep and center-of-mass trajectories accordingly to guarantee stability while accomplishing desired objectives.Several experiments on an HRP-2Kai and an HRP-4 humanoid robots are presented and discussed to illustrate and validate each of the proposed methods.
Keywords : Slam Humanoids Vision
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Submitted on : Tuesday, June 4, 2019 - 5:53:08 PM
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Arnaud Tanguy. Visual SLAM for humanoid robot localization and closed-loop control. Micro and nanotechnologies/Microelectronics. Université Montpellier, 2018. English. ⟨NNT : 2018MONTS082⟩. ⟨tel-02147610⟩



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