Apparatus and method for quantifying the surface flatness of three-dimensional point cloud data

A Light Detection and Ranging (LiDAR) system for determining surface flatness, comprising:

• a laser configured for generating laser light beam;
• a scanner configured for scanning the laser light beam along a three-dimensional (3D) target surface;
• a photodetector configured for obtaining measured points from detected reflected light from the target surface, wherein the measured points form a point cloud;
• a processor configured for:
• converting the point cloud to target surface sample coordinates along coordinate axes according to length, width, and thickness of the target surface using principal component analysis (PCA);
• computing a local mean of thickness of the target surface sample coordinates;
• computing a global mean of thickness of the target surface sample coordinates;
• computing a global standard deviation of thickness of the target surface sample coordinates;
• computing a Zα value from the local mean of thickness of the target surface sample coordinates, the global mean of thickness of the target surface sample coordinates, the global standard deviation of thickness of the target surface sample coordinates, number of local samples of the target surface sample coordinates, and a predetermined bump tolerance;
• comparing the Zα value with a Z score of a test statistic, wherein the Z score is a value that corresponds to a confidence level;
• and
• determining that a bump larger than the bump tolerance exists on the target surface and that surface flatness of the target surface is outside of the bump tolerance if the Zα value is greater than the Z score;
• producing three-dimensional measurements of the target surface based on the Zα value, thereby quantifying surface flatness of the target surface.