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Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras

机译:通过将激光,WiFi,指南针和外部摄像头网络进行强大融合,对粒子过滤器机器人进行本地化

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摘要

In this paper, we propose a multi-sensor fusion algorithm based on particle filters for mobile robot localisation in crowded environments. Our system is able to fuse the information provided by sensors placed on-board, and sensors external to the robot (off-board). We also propose a methodology for fast system deployment, map construction, and sensor calibration with a limited number of training samples. We validated our proposal experimentally with a laser range-finder, a WiFi card, a magnetic compass, and an external multi-camera network. We have carried out experiments that validate our deployment and calibration methodology. Moreover, we performed localisation experiments in controlled situations and real robot operation in social events. We obtained the best results from the fusion of all the sensors available: the precision and stability was sufficient for mobile robot localisation. No single sensor is reliable in every situation, but nevertheless our algorithm works with any subset of sensors: if a sensor is not available, the performance just degrades gracefully. (C) 2015 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了一种基于粒子滤波器的多传感器融合算法,用于在拥挤环境中的移动机器人定位。我们的系统能够融合由板载传感器和机器人外部(板外)传感器提供的信息。我们还提出了一种方法,可用于有限数量的训练样本的快速系统部署,地图构建和传感器校准。我们通过激光测距仪,WiFi卡,磁罗经和外部多摄像机网络对我们的建议进行了实验验证。我们进行了实验,验证了我们的部署和校准方法。此外,我们在受控情况下进行了本地化实验,并在社交事件中进行了实际的机器人操作。通过融合所有可用的传感器,我们获得了最佳结果:精度和稳定性足以满足移动机器人的定位要求。没有任何一种传感器在每种情况下都是可靠的,但是尽管如此,我们的算法仍适用于任何子集的传感器:如果没有可用的传感器,性能只会适度下降。 (C)2015 Elsevier B.V.保留所有权利。

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