首页> 外文会议>International Multiconference on Computer Science and Information Technology >Multilevel Localization for Mobile Sensor Network Platforms
【24h】

Multilevel Localization for Mobile Sensor Network Platforms

机译:移动传感器网络平台的多级本地化

获取原文

摘要

For a set of Mobile Sensor Network, a precise localization is required in order to maximize the utilization of Mobile Sensor Network. As well, mobile robots also need a precise localization mechanism for the same reason. In this paper, we showed a combination of various localization mechanisms. Localization can be classified in three big categories: long distance localization with low accuracy, medium distance localization with medium accuracy, and short distance localization with high accuracy. In order to present localization methods, traditional map building technologies such as grid maps or topological maps can be used. We implemented mobile sensor vehicles and composed mobile sensor network with them. Each mobile sensor vehicles act as a mobile sensor node with the facilities such as autonomous driving, obstacle detection and avoidance, map building, communication via wireless network, image processing and extensibility of multiple heterogeneous sensors. For localization, each mobile sensor vehicle has abilities of the location awareness by mobility trajectory based localization, RSSI based localization and computer vision based localization. With this set of mobile sensor network, we have the possibility to demonstrate various localization mechanisms and their effectiveness. In this paper, the preliminary result of sensor mobility trail based localization and RSSI based localization will be presented.
机译:对于一组移动传感器网络,需要精确定位以最大限度地利用移动传感器网络。同样,移动机器人也需要一个精确的本地化机制,同样是相同的原因。在本文中,我们展示了各种本地化机制的组合。本地化可以分类为三类:长距离定位,精度低,中距离,中等精度,高距离定位高精度。为了呈现本地化方法,可以使用传统地图建筑技术,如网格图或拓扑图。我们实施了移动传感器车辆并与它们组成了移动传感器网络。每个移动传感器车辆用作自动驾驶,障碍物检测和避免,地图建设,通过无线网络的沟通,图像处理和多个异构传感器的可扩展性的设施充当移动传感器节点。对于本地化,每个移动传感器车辆具有通过基于迁移轨迹的定位,基于RSSI的定位和基于计算机视觉的定位的位置意识的能力。通过这组移动传感器网络,我们有可能展示各种本地化机制及其有效性。在本文中,将介绍传感器移动跟踪的定位和基于RSSI本地化的初步结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号