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.

中文翻译：

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用于2D实时SLAM和精确定位的改进的带符号距离功能

对于许多工业机器人应用而言，准确的映射和定位非常重要。在本文中，我们针对2D SLAM和纯定位提出了一种改进的符号距离函数（SDF），以提高映射和定位的准确性。为了实现此目标，首先我们通过扩展更新范围和构建可用空间等方法改进了后端映射，以构建更准确的SDF映射。其次，为了获得更准确的前端姿势估计，我们提出了一种新的方法。通过消除激光扫描仪的随机离群值，使当前扫描与SDF子图对齐的迭代配准方法。第三，我们合并了所有SDF子图，以生成用于高度精确的纯定位的集成SDF图。实验结果表明，基于合并后的SDF图，整个地图可以实现几毫米（5mm）的定位精度。我们认为，这种方法对于在定位精度要求很高的情况下工作的移动机器人非常重要。