Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation
This work addresses reliability issues for supervisors teleoperating agricultural robots in dense crop rows, though it is incremental as it builds on existing delay-compensation methods.
The paper tackles the problem of delayed and variable frame rate video feeds in outdoor mobile robot teleoperation, proposing a modular learning-based vision pipeline that generates more accurate delay-compensated images in real-time, as demonstrated in offline evaluations compared to state-of-the-art approaches.
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.