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3D Mapping and Estimation from Moving Direction of Indoor Mobile Robot using Vanishing Points

机译:使用消失点的室内移动机器人移动方向的3D映射和估计

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There are many boundaries of artificial objects which are appeared as edges in the image. These line segments as edge impose information for understanding indoor environment. We obtain 3-dimension positional information as we use a stereo camera. There are three dominant vanishing points (VPs) in 3D world, We can separate lines and estimate forward direction by using VP. Groups of such coinciding lines which extend to the same VP are determined by angles between them. Also the crossing points transform to ICIS (Inverted Coordinates Image Space). The ICIS is proposed method to detect VPs in an infinite or finite image space. If the crossing points exist in the image, there is not transformation. However, crossing points are out of image, then, crossing points transform to ICIS. VPs are determined by distribution in ICIS. They impose maximally 3 groups. Disparity of depth direction VP calculates for its 3D position. Among them, we need to determine a dominant VP which comes from vertical lines for corresponding. To correspond the line segments, the smallest SAD (Sum of Absolute Difference) value used beside area of them. We also compare the order and direction of lines. Disparities from corresponding lines between stereo images are calculated for this so that 3D position is calculated by triangulation. Evading from the perspective effect which occur 3D scene projects into 2D image thus size or angle changes, geometric ratios are used for rectifying such changes. We already know 3D position of depth direction VP and vertical lines before rectifying. So, rectification is to add before rectifying values to obtained values which are geometric ratio between VP and vertical lines. 3D map is obtained from rectifying 3D position. Also, moving direction estimates from angle between VP and principal point. We experiment several slant, tilt or pan images. VP and principal point detected, when the robot also looked at floor or wall.
机译:存在许多人造物体的边界,其被出现为图像中的边缘。这些线段作为边缘施加了理解室内环境的信息。我们使用立体声相机获得3维定位信息。 3D世界上有三个主导的消失点(VPS),我们可以使用VP分开线条和估计前进方向。延伸到相同VP的这种重合线的组由它们之间的角度确定。还交叉点转换为ICIS(倒置坐标图像空间)。提出ICIS在无限或有限图像空间中检测VPS的方法。如果图像中存在交叉点,则没有转换。但是,交叉点脱离了图像,然后交叉点转换为ICIS。 VPS由ICIS分发确定。它们施加最大3组。深度方向VP的差异计算其3D位置。其中,我们需要确定来自垂直线的主导VP,以进行相应的。要对应行段,最小的悲伤(绝对差异和绝对差异)在它们的区域旁边使用。我们还比较线的顺序和方向。计算来自立体图像之间的相应线的差异,以便通过三角测量计算3D位置。从将3D场景突出的透视效果延伸到2D图像的尺寸或角度变化,几何比例用于整流这些变化。在整流之前,我们已经知道深度方向VP和垂直线的3D位置。因此,整改是在整流值之前添加到获得的值,该值是VP和垂直线之间的几何比率。 3D地图是从整流3D位置获得的。此外,移动方向从VP和主点之间的角度估计。我们尝试几个倾斜,倾斜或平移图像。检测到的vp和主点,当机器人也看着地板或墙壁。

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