Accurate mapping and localization are very important for many industrial robotics applications. In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization. To achieve this goal, firstly we improved the back-end mapping to build a more accurate SDF map by extending the update range and building free space, etc. Secondly, to get more accurate pose estimation for the front-end, we proposed a new iterative registration method to align the current scan to the SDF submap by removing random outliers of laser scanners. Thirdly, we merged all the SDF submaps to produce an integrated SDF map for highly accurate pure localization. Experimental results show that based on the merged SDF map, a localization accuracy of a few millimeters (5mm) can be achieved globally within the map. We believe that this method is important for mobile robots working in scenarios where high localization accuracy matters.

翻译：精确的绘图和本地化对于许多工业机器人应用非常重要。 在本文中,我们建议改进2D SLAM和纯本地化的签名远程功能(SDF),以提高绘图和本地化的准确性。为了实现这一目标,首先,我们改进后端绘图,通过扩大更新范围,建造自由空间等,建立一个更准确的SDF地图。第二,为了更准确地对前端进行估计,我们提议一种新的迭代注册方法,通过删除激光扫描的随机离线,使当前扫描与SDF子图相匹配。第三,我们合并了SDF的所有子图,以制作一个综合的SDFM地图,用于高度准确的本地化。实验结果显示,根据合并的SDF地图,可以在地图范围内实现几毫米(5毫米)的本地化精度。我们认为,对于在高度本地化精确性重要的情况下运行的移动机器人,这种方法很重要。