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A Hierarchical Searching Approach for Laser SLAM Re-localization on Mobile Robot Platform

机译:移动机器人平台上激光SLAM重新定位的分层搜索方法

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Simultaneous localization and mapping (SLAM) is an important issue for mobile robot system. Re-localization, as a part of close loop checking of SLAM, focuses on localize the position of a robot agent when it is restarted. Traditional localization methods couldn't reach both a high accuracy and a little time cost. In this paper, we proposed a newly hierarchical searching approach for laser SLAM re-localization. We do hierarchically searching to match an input laser frame with the whole 2-D map, instead of comparing it with each frame in the map. By using such approach, we could accelerate re-localization speed, so the performance of SLAM is improved. Our SLAM system is well organized on a mobile robot platform. After field test, our approach reaches a average precision (AP) of 90.6% on a 20m×20m map area, and the time cost is averagely 30ms. The result shows our hierarchical searching approach is effective for re-localization problems on laser SLAM.
机译:同步定位和地图绘制(SLAM)是移动机器人系统的重要问题。重新定位是SLAM闭环检查的一部分,着重于在重新启动机器人代理程序时对其进行本地化。传统的本地化方法无法同时达到很高的精度和很少的时间成本。在本文中,我们提出了一种用于激光SLAM重新定位的新的分层搜索方法。我们进行分层搜索以使输入激光帧与整个二维地图匹配,而不是将其与地图中的每个帧进行比较。通过使用这种方法,我们可以加快重新定位的速度,从而提高了SLAM的性能。我们的SLAM系统在移动机器人平台上井井有条。经过现场测试,我们的方法在20m×20m的地图区域上达到90.6%的平均精度(AP),时间成本平均为30ms。结果表明,我们的分层搜索方法对于解决激光SLAM上的重新定位问题是有效的。

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