这是一种令人尬的简单方法,用于森林环境的视觉导航.
Chaoyue Niu1, Callum Newlands1, Klaus-Peter Zauner1
1School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.
Frontiers in robotics and AI
|July 14, 2023
概括
这项研究介绍了小型森林机器人的低成本导航系统,使用轻量级的神经网络来预测地形深度. 该系统在现实世界的试验中成功地驾驶了超过750米的具有挑战性的森林地形.
科学领域:
- 现场机器人 现场机器人
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于未知的地形,各种障碍物和不断变化的环境条件,森林导航给野外机器人带来了重大挑战.
- 小型,低视角机器人面临阻塞问题,与通常用于越野导航研究的更大,更昂贵的系统不同.
- 现有的导航系统通常依赖于昂贵的传感器,限制了小规模机器人应用的可访问性.
研究的目的:
- 设计和评估一个低成本的,基于视觉的导航系统,用于森林环境中小型漫游车.
- 通过复杂的自然地形实现便携式机器人的自主或半自主导航.
- 为了解决小型机器人平台中低视角和传感器封闭的局限性.
主要方法:
- 使用轻型卷积神经网络 (CNN) 来预测来自低视角RGB摄像头输入的深度图像.
- 一个粗的导航算法处理了预测的深度信息,以确定可穿越的路径并避免障碍物.
- 该系统在手动推送的移动平台上使用Raspberry Pi进行了测试,包括高保真模拟和现实实地测试.
主要成果:
- 导航系统成功地引导移动平台在750米的不同森林地形上,包括灌木,倒下的树木和不平坦的地面.
- 导航使用最小输入 (16x16深度预测从32x32 RGB图像) 实现,并在各种天气和照明条件下证明了稳定性.
- 该算法表现出对摄像头角度变化,运动模糊,低光和高对比度照明的适应性.
结论:
- 一个低成本,基于视觉的导航系统是可行的小森林漫游者,克服低视角和复杂的地形的挑战.
- 开发的系统为在非结构化的森林环境中进行机器人探索和监测提供了实用解决方案.
- 该方法展示了资源有限的机器人在具有挑战性的自然景观中有效导航的潜力.
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