Floor sensing system using laser range finder and mirror for localizing daily life commodities

Yasunobu Nohara, T. Hasegawa, K. Murakami
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引用次数: 13

Abstract

This paper proposes a new method of measuring position of daily commodities placed on a floor. Picking up an object on a floor will be a typical task for a robot working in our daily life environment. However, it is difficult for a robotic vision to find a small daily life object left on a large floor. The floor surface may have various texture and shadow, while other furniture may obstruct the vision. Various objects may also exist on the floor. Moreover, the surface of the object has various optical characteristics: color, metallic reflection, transparent, black etc. Our method uses a laser range finder (LRF) together with a mirror installed on the wall very close to floor. The LRF scans the laser beam horizontally just above the floor and measure the distance to the object. Some beams are reflected by the mirror and measure the distance of the object from virtually different origin. Even if the LRF fails two measurements, the method calculates the position of the object by utilizing information that the two measurements are unavailable. Thus, the method achieves two major advantages: 1) robust against occlusion and 2) applicable to variety of daily life commodities. In the experiment, success rate of observation of our method achieves 100% for any daily commodity, while that of the existing method for a cell-phone is 69.4%.
地板感应系统利用激光测距仪和镜子定位日常生活用品
本文提出了一种测量地板上日常物品摆放位置的新方法。在我们的日常生活环境中,捡起地板上的物体将是机器人的典型任务。然而,机器人视觉很难找到留在大地板上的小型日常生活物品。地板表面可能有各种纹理和阴影,而其他家具可能会阻碍视觉。地板上也可能存在各种各样的物体。而且,物体表面具有各种光学特性:彩色、金属反射、透明、黑色等。我们的方法使用激光测距仪(LRF)和安装在离地面很近的墙上的镜子。LRF在地板上方水平扫描激光束,并测量到物体的距离。一些光束被镜子反射,测量出物体从不同原点的距离。即使LRF两次测量失败,该方法也会利用两次测量无法获得的信息来计算对象的位置。因此,该方法具有两个主要优点:1)抗遮挡能力强;2)适用于各种日常生活商品。在实验中,我们的方法对任何一种日用品的观察成功率都达到100%,而现有方法对一部手机的观察成功率为69.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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