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从稀疏的视觉测距进行密集映射:一种轻量级的不确定性保证深度完成方法.

Daolong Yang1, Xudong Zhang1, Haoyuan Liu1

  • 1School of Mechanical Engineering and Automation, Beihang University, Beijing, China.

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概括
此摘要是机器生成的。

这项研究引入了一种轻量级的图像引导的不确定性意识深度完成网络 (IU-DC),用稀疏的视觉距离测量 (VO) 数据创建密集的地图. 新方法显著提高了绘图准确度和覆盖范围,同时使用的参数比现有方法少.

关键词:
深度学习用于视觉感知.在深度完成完成.绘制地图,绘制地图.不确定性估计估计的不确定性视觉测距仪使用视觉测距仪.

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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:

  • 视觉测距 (VO) 对于移动机器人空间感知至关重要,但由于深度数据不足,会产生稀疏的地图.
  • 现有的深度完成方法与极端稀疏性和VO.深度数据的不均分布作斗争.

研究的目的:

  • 从稀疏的VO数据开发一个轻量级的网络来完成密集的深度地图.
  • 为了提高移动机器人的空间感知精度和绘图能力.

主要方法:

  • 提出了一个图像引导的不确定性意识深度完成网络 (IU-DC).
  • 将颜色和空间信息集成到一个正常化的卷积神经网络中.
  • 该网络输出密集深度图和相关的不确定性估计.

主要成果:

  • 与最先进的 (SOTA) 方法相比,IU-DC在深度和不确定性估计准确度方面表现优越.
  • 现实世界绘图任务显示,重建的卷数增加了50倍,地面真相覆盖率达到78%,准确度提高了两倍.
  • IU-DC网络很轻,只有0.6M的参数 (3%的SOTA).

结论:

  • IU-DC有效地解决了VO中稀疏深度数据的挑战,以增强空间感知.
  • 不确定性意识输出使得异常值的过能够进行更精确的映射.
  • 拟议的方法为机器人绘图的效率和性能提供了显著的改善.