Improved Signed Distance Function for 2D Real-time SLAM and Accurate Localization

TL;DR

This paper introduces an improved Signed Distance Function (SDF) approach for 2D SLAM and localization, enhancing mapping accuracy and achieving millimeter-level localization precision in real-time robotic applications.

Contribution

The paper presents novel enhancements to SDF mapping, a new iterative registration method for pose estimation, and a technique to merge submaps for highly accurate localization.

Findings

Achieved 5mm global localization accuracy.

Enhanced SDF map accuracy through extended update range.

Effective merging of submaps for precise localization.

Abstract

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.