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A nonparametric learning approach to range sensing from omnidirectional vision

机译：一种非参数学习方法，可从全向视觉进行距离感测

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摘要

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration-tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.
机译：我们提出一种新颖的方法，通过学习在训练阶段可用的视觉特征和距离测量之间的关系，从单个全向摄像机图像估计深度。我们的模型不仅得出各个方向与障碍物最可能的距离，而且得出这些估计的预测不确定性。移动机器人可以利用此信息来构建环境的占用栅格图，或在通常需要专用接近传感器（例如激光测距仪或声纳）的勘探任务中避开障碍物。我们在本文中展示了如何使用全向摄像机替代此类距离传感器。作为学习引擎，我们使用高斯过程，一种非参数方法进行函数回归，以及最近开发的扩展，用于处理与输入相关的噪声。在配备了全向摄像头系统的移动机器人在不同室内环境中进行的实际实验中，我们证明了我们的系统能够以与基于声纳或红外光的专用传感器相当的精度估算距离。

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