Automated 360-degree dense point object inspection

An object inspection system, the system comprising:

• a scanning unit;
• and
• a processor configured receive the captured point clouds from the scanning unit, and to:
• determine a system calibration transform;
• determine a recommended scan set-up parameters for an object to be scanned;
• instruct the scanning unit to perform at least one scan of the object, wherein the at least one scan includes capturing two or more point clouds representing the same view of the scanned object and two or more point clouds representing different views of the scanned object;
• receive the captured point clouds from the scanning unit;
• determine a three-dimensional registration between the two or more point clouds representing the same view of the object;
• determine a three-dimensional registration between the two or more point clouds representing different views of the object;
• and
• monitor the performance of the scanning unit.

A method for calibrating an object inspection system comprising:

• scanning a calibration target to obtain a point cloud representing the target;
• tessellating the point cloud into one or more tiles containing a plurality of points of the point cloud;
• for each of the one or more tiles, fitting a plane model to the points contained in the tile, wherein the plane model includes value of a three-dimensional vector normal to the surface of the plane and the distance of the plane from a coordinate system reference point;
• adding the normal vectors from each of the one or more tiles and calculating the average of the collection of normal vectors;
• using average normal vector orientation to define a reference plane outside the point cloud and calculating each cloud point to reference plane;
• using a histogram to find local maximums, and creating a group of points each with a distance to the defined reference plane that falls within a tolerance threshold to the local maximum, wherein each of these groups is a planar segment of the point cloud corresponding to a detected plane of the target;
• for each planar segment, fitting a plane model, and calculating the normal value of the segmented points;
• calculating the average of the normal values weighted by the population of points in each planar segment;
• and applying the average normal vector to each of the planar segments, and recalculate the distance from the coordinate origin for each plane;
• and monitor the performance of the scanning.