利用有源信标设计低成本室内定位系统的挑战

Jens-Steffen Gutmann, P. Fong, Lihu Chiu, Mario E. Munich
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引用次数: 13

摘要

定位对移动机器人的目标导航非常有帮助。在这篇文章中,我们描述了我们在设计一个低成本的室内定位系统时所面临的挑战,该系统可以用于消费者和家用机器人,用于家庭环境中的系统导航。我们的系统使用主动信标,将一对红外线(IR)点投射到天花板上,机器人上有一个传感器来观察它们。为了降低成本,我们设计了一个3二极管传感器,即一个3像素的相机,测量到斑点的方向。与典型的VGA或更高分辨率的相机相比,可以精确跟踪信标点,我们的低成本传感器受到多路径的影响,即光不仅可以直接到达传感器,还可以通过墙壁和其他家具的反射。我们通过一种定位方法来解决这个问题,该方法通过同时定位和映射(SLAM)方法来学习房间内的光分布。我们的实验提供了该方法的准确性和一致性的数字。该系统是在我们的Mint机器人上实现的,该机器人配备了ARM 7处理器和64kbyte的RAM,用于自动清洁地板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges of designing a low-cost indoor localization system using active beacons
Localization is very helpful for goal-oriented navigation of a mobile robot. In this article, we describe the challenges we faced when designing a low-cost indoor localization system that can be employed on consumer and domestic robots for the systematic navigation in household environments. Our system uses active beacons that project a pair of infrared (IR) spots onto the ceiling and a sensor on the robot which observes them. In order to reduce cost, we designed a 3-diode sensor, i.e. a three pixel camera, that measures directions to the spots. In contrast to e.g. a typical camera with VGA or higher resolution that would allow for a precise tracking of the beacon spots, our low-cost sensor suffers from multi-path, i.e. light not only reaches the sensor directly but also through reflections from walls and other furniture. We tackle this problem by a localization method that learns the light distribution in the room through a simultaneous localization and mapping (SLAM) approach. Our experiments provide numbers on the accuracy and consistency of this method. The presented system is implemented on our Mint robot equipped with an ARM 7 processor and 64 kByte of RAM for the autonomous cleaning of floors.
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