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Particle Filter for Reliable Estimation of the Ground Plane from Depth Images in a Travel Aid for the Blind

机译：用于从盲援助的旅行援助中的深度图像可靠地估计地面平面的可靠估计

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This paper presents a reliable method for the segmentation of image sequences of 3D scenes used in an electronic travel aid for the blind. We propose an implementation of the particle filtering (PF) algorithm to estimate and track the orientation and position of the ground plane free of obstacles. We explain how the state vector in the PF algorithm was defined, and verify the results on a large set of indoor and outdoor sequences, shot by a moving stereovision camera. The movement of the camera is not restricted, and is capable of six degrees of freedom (DoF). We show that ground plane orientation root mean square (RMS) error estimates do not exceed 2 or 3°, for the roll and pitch angles, respectively. The overlap between the mean values for the Jaccard similarity coefficient of the detected ground-truth ground plane regions and the ground plane regions was 0.94. Although the method was developed for use in an electronic travel aid for the blind, it could also find applications in the automatic navigation of autonomous vehicles and unmanned aerial vehicles (UAVs).
机译：本文提出了一种可靠的方法，用于对盲人的电子旅行援助中使用的3D场景进行分割的可靠方法。我们提出了一种实现粒子过滤（PF）算法来估计和跟踪地面平面的定向和位置，无障碍物。我们解释了PF算法中的状态向量如何定义，并通过移动的立体相机拍摄的大量室内和室外序列上的结果。相机的运动不受限制，并且能够六度自由（DOF）。对于卷和俯仰角度，我们表明接地平面取向均方根（RMS）误差估计不超过2或3°。检测到的地面接地平面区域和接地平面区域的Jaccard相似性系数之间的平均值之间的重叠为0.94。虽然该方法是用于盲人的电子旅行辅助工具，但也可以在自动车辆和无人驾驶飞行器（无人机）的自动导航中找到应用。

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