  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.
