Fast 2D Map Matching Based on Area Graphs
This work addresses map matching for robotics or mapping applications, offering improvements in speed and scalability for large-scale maps, but it appears incremental as it builds on existing area graph methods.
The paper tackles the problem of merging two different 2D grid maps by proposing a novel area matching algorithm based on area segmentation, which transfers maps to an area graph representation and uses voting to compute results. The experiments show that the algorithm achieves better performance on large-scale maps and faster computation speed compared to a state-of-the-art method.
We present a novel area matching algorithm for merging two different 2D grid maps. There are many approaches to address this problem, nevertheless, most previous work is built on some assumptions, such as rigid transformation, or similar scale and modalities of two maps. In this work we propose a 2D map matching algorithm based on area segmentation. We transfer general 2D occupancy grid maps to an area graph representation, then compute the correct results by voting in that space. In the experiments, we compare with a state-of-the-art method applied to the matching of sensor maps with ground truth layout maps. The experiment shows that our algorithm has a better performance on large-scale maps and a faster computation speed.