Improved Feature Map Merging Using Virtual Supporting Lines for Multi-Robot Systems

Abstract

This paper addresses the problem of feature map merging, which is one of the essential techniques for multi-robot systems. If inter-robot measurements are not available for feature map merging, the only way to obtain the map transformation matrix is feature map matching. However, the conventional feature map matching technique requires too much computation time because it has to be iteratively performed to compute the degree of the mismatch between multiple feature maps. This paper proposes a non-iterative feature map merging technique using virtual supporting lines (VSLs) which is also accurate and robust. The proposed technique extracts the spectral information of multiple feature maps using VSLs and obtains the map transformation matrix using the circular cross-correlation between the extracted spectral information of the multiple feature maps. The proposed technique was tested on feature maps produced by experiments with vision sensors, which was performed non-iteratively. In addition, it consistently showed a high acceptance index, which indicates the degree of accuracy for feature map merging.