交叉视角语义映射:整合人类和机器人的视角,改进3D语义重建
László Kopácsi1,2, Benjámin Baffy2, Gábor Baranyi2
1Department of Interactive Machine Learning, German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种机器人创建人类可理解的3D语义地图的方法,通过语义匹配和深度学习克服观点差异,以改善机器人的感知和人机交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 全中心的语义3D地图对于人机交互至关重要,使自我中心的观点推导成为可能.
- 类标签和地图解释的差异源于人类和机器人的不同视角,特别是小机器人.
- 现有的3D语义重建管道与视角变化作斗争.
研究的目的:
- 扩展3D语义重建管道,使用交叉视角语义匹配来实现人机协作.
- 开发用于从不寻常的低角度机器人视角获得语义标签的方法.
- 为了使小机器人能够为人类合作伙伴生成高质量的语义地图.
主要方法:
- 扩展实时3D语义重建管道,在人类和机器人视角之间进行语义匹配.
- 利用深度识别网络并提出从非标准视角获得语义标签的新方法.
- 通过超像素细分和环境几何学,将人类视角的语义重建适应到机器人视角.
主要成果:
- 从机器人的角度实现了高质量的语义细分,与人类观点相提并论.
- 通过利用获得的语义信息,改善了低视角的深度网络识别性能.
- 展示了一个小型机器人能够自主生成高质量的语义地图的能力.
结论:
- 拟议的方法有效地弥合了3D绘图中人类和机器人的观点之间的语义差距.
- 使小机器人能够创建准确和有用的语义地图,促进更好的人机交互.
- 实时性能促进了机器人和增强现实的交互应用.
关键词:
三维语义地图 3D语义地图计算机视觉 计算机视觉深度学习是一种深度学习.人类与机器人的协作.标签转移 标签转移 标签转移实时重建的重建.语义匹配是指语义上的匹配.语义细分 语义细分 语义细分 语义细分超像素细分的超像素细分.更多相关视频
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