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Obstacle Detection of a Humanoid on a Plane Using a Relative Disparity Map Obtained by a Small Range Image Sensor

机译:使用小距离图像传感器获得的相对视差图在平面上对人形机器人进行障碍物检测

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In this paper, methods for detecting obstacles on a plane using a relative disparity map (RDMap) are proposed and discussed. The RDMap, which was formerly introduced by the author Umeda, is relative to a plane that is observed at first as the reference. It has an interesting feature that a plane in real 3D space also becomes a plane in the map, and has homogeneous characteristics compared to an ordinary range image. The proposed methods work even when the pose of the sensor changes significantly, which is the case in humanoid walking. First, a method to detect planar regions and obstacles by fitting a plane to the RDMap and a method to obtain the pose parameters from the RDMap are introduced. Fundamental experiments are then conducted to verify that a plane in real 3D space becomes a plane in the RDMap and that obstacles can be detected using the residual sum of squares for the fitted plane, and measurement errors in pose parameters are then evaluated. Finally, an experimental system with a humanoid and a small range image sensor is constructed, and it is demonstrated that the humanoid can detect obstacles on a plane by the proposed methods while walking.
机译:在本文中,提出并讨论了使用相对视差图(RDMap)来检测平面上的障碍物的方法。 RDMap是以前由作者梅田(Umeda)引入的,它相对于最初作为参考观察的平面。它具有一个有趣的功能,即真实3D空间中的平面也将成为地图中的平面,并且与普通的距离图像相比具有同质的特征。所提出的方法即使在传感器的姿势发生显着变化时也能正常工作,在类人动物行走中就是这种情况。首先,介绍了通过将平面拟合到RDMap来检测平面区域和障碍物的方法以及从RDMap获得姿态参数的方法。然后进行基础实验,以验证真实3D空间中的平面成为RDMap中的平面,并且可以使用拟合平面的平方和剩余量来检测障碍物,然后评估姿势参数中的测量误差。最后,建立了一个具有人形机器人和小范围图像传感器的实验系统,并证明了拟人机器人在行走时可以通过所提出的方法检测到飞机上的障碍物。

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