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Conference Papers Year : 2022

Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM

Abstract

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these drawbacks, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows the effectiveness of our solution.

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Dates and versions

hal-03843147 , version 1 (08-11-2022)

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Yang Chen, Leyuan Sun, Mehdi Benallegue, Rafael Cisneros-Limon, Rohan Singh, et al.. Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM. Humanoids 2022 - 21st IEEE-RAS International Conference on Humanoid Robots, Nov 2022, Ginowan, Japan. pp.306-313, ⟨10.1109/Humanoids53995.2022.9999740⟩. ⟨hal-03843147⟩
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