Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation

TL;DR

This paper presents a modular, learning-based vision pipeline that generates delay-compensated video feeds in real-time, improving teleoperation of outdoor agricultural robots under challenging environmental and network conditions.

Contribution

It introduces a novel real-time, delay-compensation method evaluated in outdoor field environments, outperforming existing approaches and addressing practical teleoperation challenges.

Findings

More accurate images than state-of-the-art methods

Real-time delay compensation demonstrated in outdoor environments

Provides videos and code for reproducibility

Abstract

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.