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林业导航机的视觉系统。

Vision System for a Forestry Navigation Machine.

机构信息

Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal.

ADAI (Associação para o Desenvolvimento da Aerodinâmica Industrial), Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal.

出版信息

Sensors (Basel). 2024 Feb 24;24(5):1475. doi: 10.3390/s24051475.

DOI:10.3390/s24051475
PMID:38475010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10933840/
Abstract

This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

摘要

本文提出了一种视觉系统的开发，旨在增强机器人在复杂森林环境中的自主导航能力。该系统利用 RGBD 和热成像摄像机，特别是英特尔 RealSense 435i 和 FLIR ADK，将各种视觉传感器与先进的图像处理算法集成在一起。这种集成使机器人能够实时做出决策，识别障碍物，并在操作过程中动态调整轨迹。本文重点介绍了系统的架构方面，强调了传感器的作用和算法的制定对于确保机器人在具有挑战性的森林地形中安全导航的关键作用。此外，本文还讨论了专门针对森林环境训练的两个数据集，旨在评估它们对自主导航的影响。在真实的森林环境中进行的测试证实了所开发的视觉系统的有效性。结果突出了该系统对机器人在森林环境中自主导航的重要贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/7827d1da3de3/sensors-24-01475-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/f4a7adec3177/sensors-24-01475-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/be99297b9553/sensors-24-01475-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/62e2cd89b098/sensors-24-01475-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/e040def107b6/sensors-24-01475-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/b11c3ba3ded7/sensors-24-01475-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/b93045174b84/sensors-24-01475-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/38e27a8993e1/sensors-24-01475-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/f4a7adec3177/sensors-24-01475-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/be99297b9553/sensors-24-01475-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/62e2cd89b098/sensors-24-01475-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/e040def107b6/sensors-24-01475-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/da47c1b6d755/sensors-24-01475-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/47e9ae1848ef/sensors-24-01475-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbf/10933840/7827d1da3de3/sensors-24-01475-g014.jpg

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本文引用的文献

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Sensors (Basel). 2023 Dec 15;23(24):9853. doi: 10.3390/s23249853.
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Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV.通过半自主四足机器人和六旋翼无人机评估森林生态系统。
Sensors (Basel). 2022 Jul 23;22(15):5497. doi: 10.3390/s22155497.
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Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions.自主热成像搜救机器人系统，用于遇难者识别。
Sensors (Basel). 2021 Nov 4;21(21):7346. doi: 10.3390/s21217346.
4
Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics.基于可见和热成像的深度学习树干检测在林业移动机器人中的应用
J Imaging. 2021 Sep 3;7(9):176. doi: 10.3390/jimaging7090176.
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The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review.智能地面车辆在全天气条件下的感知系统：系统文献综述。
Sensors (Basel). 2020 Nov 15;20(22):6532. doi: 10.3390/s20226532.