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Simultaneous Localization and Mapping with Application to Monitoring of Underground Transportation Infrastructure

机译:同时定位与制图在地下交通基础设施监测中的应用

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This paper presents the results of development, design and implementation of a Simultaneous Localization And Mapping (SLAM) system for autonomous real-time localization with application to Underground Transportation Infrastructure (UTI) such as tunnels. Localization is achieved in absence of any GPS or auxiliary system. The system is a necessary element of a fully autonomous platform for detection of cracks and other anomalies on the interior surfaces of tunnels and other UTI. It can be used for tagging of high-resolution sensor data obtained with low-cost prototype data acquisition platforms previously developed [1], [2]. Visual based SLAM has been used as the core element in an architecture also employing a Commercial Off The Shelf (COTS) ZED stereocamera from Stereolabs. To achieve real-time operation, an NVIDIA Jetson Tx2 massively parallel Graphic Processing Unit (GPU) has been used as the core computational engine employing two different software libraries. We achieved localization at 5 frames per second (fps) using ORBSLAM2 open source software library, while the much lighter but proprietary ZED SDK was able to deliver performance at nearly 60 fps.
机译:本文介绍了用于实时实时定位的同时定位和制图(SLAM)系统的开发,设计和实现的结果,并将其应用于隧道等地下交通基础设施(UTI)。在没有任何GPS或辅助系统的情况下就可以实现定位。该系统是用于检测隧道和其他UTI内表面上的裂缝和其他异常情况的全自动平台的必要元素。它可用于标记通过先前开发的低成本原型数据采集平台获得的高分辨率传感器数据[1],[2]。基于视觉的SLAM已被用作架构中的核心元素,该架构也采用了Stereolabs的商业现货(COTS)ZED立体相机。为了实现实时操作,NVIDIA Jetson Tx2大规模并行图形处理单元(GPU)已被用作采用两个不同软件库的核心计算引擎。我们使用ORBSLAM2开源软件库以每秒5帧(fps)的速度实现了本地化,而重量更轻但专有的ZED SDK能够以近60 fps的速度提供性能。

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