High Resolution Grid Map with Normal Distribution Transform Algorithm

Abstract

A new convergence calculation method of the Normal Distributions Transform (NDT) scan matching for high resolution of grid maps is proposed. NDT scan matching algorithm usually has a good effect on large grids, so it is difficult to generate the detailed map with small grids. The proposed method employs Interactive Closest Point(ICP) algorithm to find corresponding point, and it also enlarges the convergence area by modifying the eigenvalue of normal distribution so that the evaluation value is driven effectively for the pairing data. In addition, outlier elimination process is implemented to the scanning for sub-grid scale objects. The scanning data from Laser Range Finder(LRF) have error but its set of detected small objects can be clustered to determine the Center of Mass(CoM) and the outlier data. The outlier commonly locates behind true points and it can be eliminated when the robot observes from other points. Experimental result shows the effectiveness of the proposed convergence algorithm and the outlier elimination method.