一个可行区域边界数据集用于野外机器人的视觉导航
Kai Zhang1, Xia Yuan2, Jiachen Xu3
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210000, China.
Scientific data
|January 28, 2025
概括
一个带有指导语义和形状标签的新数据集,改善了在复杂道路上导航的机器人的可行区域边界检测. 这增强了实时视觉导航系统,而不需要详细的先前地图.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 现有的道路边界数据集缺乏用于机器人导航的详细语义.
- 目前的边界探测器与复杂的道路,阻塞和各种形状作斗争.
- 提取指导路线和边界形状需要具有挑战性的后处理.
研究的目的:
- 引入一个新的数据集,用于可行区域边界检测.
- 为道路界限提供更丰富的语义和形状信息.
- 促进先进边界检测模型的培训和评估.
主要方法:
- 一个可行区域边界数据集的半自动构建.
- 从多样化和不规则的道路几何形状 (曲,十字路口,T结) 收集数据.
- 将新的指导语义,形状和场景复杂性标签分配给边界.
主要成果:
- 该数据集包括82.05%的复杂和不规则的道路结构数据.
- 该数据集使边界探测器的公平培训,评估和比较成为可能.
- 促进实时视觉导航和道路边界检测任务的开发.
结论:
- 新的数据集解决了现有的道路边界数据的局限性.
- 它支持开发更强大,更准确的可行区域边界探测器.
- 这一进步对于在未绘制的环境中实时机器人导航至关重要.
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