越野感知地形分类方法研究进展与趋势

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Tanzila Arafin, Anwar Hosen, Zoran Najdovski, Lei Wei, Mohammad Rokonuzzaman, Michael Johnstone
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引用次数: 0

摘要

在农业、军事和勘探应用中,越野自动驾驶汽车(oav)在具有挑战性的环境中越来越受欢迎。这些车辆面临着独特的挑战,如不可预测的地形、动态障碍物和变化的环境条件。因此,有一个高效的地形分类系统是保证无人机安全高效运行的必要条件。本文概述了越野地形分类方法的最新进展和新趋势。通过全面的文献综述,本研究探讨了利用地形的外观和几何形状进行分类任务的传感器模式和技术的使用。该研究讨论了基于学习的方法,特别是深度学习,并强调了通过混合多模态技术集成多种传感器模式。最后,本研究回顾了现有的非道路数据集,并探讨了不同自治域的地形分类用例和应用。鉴于地形分类的快速发展,本文组织和调查提供了一个全面的概述。通过对当前景观的结构化回顾,本文大大提高了我们对非结构化环境中地形分类的理解,同时也强调了未来研究的重要领域,特别是基于深度学习的进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advances and Trends in Terrain Classification Methods for Off-Road Perception

Advances and Trends in Terrain Classification Methods for Off-Road Perception

Off-road autonomous vehicles (OAVs) are becoming increasingly popular for navigating challenging environments in agriculture, military, and exploration applications. These vehicles face unique challenges, such as unpredictable terrain, dynamic obstacles, and varying environmental conditions. Therefore, it is essential to have an efficient terrain classification system to ensure safe and efficient operation of OAVs. This paper provides an overview of recent advances and emerging trends in off-road terrain classification methods. Through a comprehensive literature review, this study explores the use of sensor modalities and techniques that leverage both appearance and geometry of the terrain for classification tasks. The study discusses learning-based approaches, particularly deep learning, and highlights the integration of multiple sensor modalities through hybrid multimodal techniques. Finally, this study reviews the available off-road datasets and explores the use cases and applications of terrain classification across various autonomous domains. Given the rapid advancements in terrain classification, this paper organizes and surveys to provide a comprehensive overview. By offering a structured review of the current landscape, this paper significantly enhances our understanding of terrain classification in unstructured environments, while also highlighting important areas for future research, particularly in deep-learning-based advancements.

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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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