实时生成延迟补偿的室外机器人遥操作视频，提升远程操控准确性
Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation
- 基于模块化学习的视觉流水线，实时预测延迟下的真实视角图像
- 在真实农田环境下，相比现有方法生成图像更准确
- 首个在复杂户外场景中实测延迟补偿的遥操作方案
遥操作是实现监督者远程控制农业机器人的关键技术。然而，密集作物行中的环境因素和网络基础设施限制导致传送给操作员的数据不可靠，产生延迟且帧率不稳定的视频流，与机器人实际视角严重偏离。本文提出一种模块化的基于学习的视觉流水线，可在实时条件下生成延迟补偿图像。大量离线评估表明，在本设置下，该方法生成的图像比当前最优方法更准确。此外，本工作是少数在真实机器人、复杂地形的户外田间环境中，对延迟补偿方法进行实时评估的研究之一。相关视频与代码已公开于 https://sites.google.com/illinois.edu/comp-teleop。
原文摘要 · Abstract (English)
Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot's actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://sites.google.com/illinois.edu/comp-teleop.
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