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

2024-09-16

arXiv.org

AI Summary

To address high latency, low frame rate, and perspective distortion in teleoperated video streaming under weak-network conditions in dense-crop-field environments, this paper proposes a modular learning-based visual pipeline for end-to-end real-time video latency compensation. The method integrates robot pose estimation, optical-flow-guided motion modeling, and adaptive temporal alignment for view synthesis, yielding a lightweight, deployable architecture. It achieves, for the first time in realistic outdoor agricultural settings, sub-100 ms end-to-end latency compensation with stable 30 fps output; offline evaluation shows a 23.6% reduction in image reconstruction error over state-of-the-art methods. Key contributions are: (1) the first real-time visual latency compensation framework tailored for weak-network agricultural robots; and (2) a modular learning design that jointly optimizes accuracy, robustness, and embedded-system deployment efficiency.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Real-time delay-compensated video generation
• Outdoor mobile robot teleoperation
• Overcoming network and environmental limitations

Innovation

• Modular learning-based vision pipeline
• Real-time delay-compensated image generation
• Outdoor field environment evaluation