NDT scan matching method for high resolution grid map

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 object. The scanning data from laser range finder (LRF) have error but its set of detected small object 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 point. Experimental result shows the effectiveness of the proposed convergence algorithm and outlier elimination method.

Authors

• Tomohito Takubo
• Takuya Kaminade
• Yasushi Mae
• Kenichi Ohara
• Tatsuo Arai

Keywords

• Convergence
• Gaussian distribution
• Iterative closest point algorithm
• Iterative algorithms
• Eigenvalues and eigenfunctions
• Simultaneous localization and mapping
• Intelligent robots
• USA Councils
• Mesh generation
• Object detection
• Grid Map
• Normal Distribution Transform
• Scan Matching
• Normal Distribution
• Center Of Mass
• Small Objects
• Detailed Mapping
• Convergence Of Algorithm
• Scan Data
• Laser Ranging
• Small Grid
• Outlier Elimination
• Covariance Matrix
• Object Recognition
• Expansion Coefficient
• Grid Size
• Wall Surface
• Amount Of Computation
• Gridded Data
• Direct Radiation
• Input Point
• Edges Of Objects
• Reference Scan
• Reference Grid
• Points In Cells
• Dead Reckoning
• Environment Map
• False Recognition
• Input Error