Abstract:To realize online detection of local flatness of workpieces with planar features (e.g., cap of power battery), a flatness detection method based on point cloud is proposed. First, the point cloud is acquired by a 3D laserscanning sensor, and the unit normal vector of the main plane in the workpiece is achieved by a clustering algorithm. The point cloud is rotated to fulfill tilt of the point cloud alignment. Then, the rotated point cloud is transformed into a grayscale image. Template matching is performed with the grayscale image of the standard workpiece to obtain the offset angle and deflection angle between the measured workpiece and the standard workpiece. In this way, the point cloud is calibrated. A point set is intercepted from the aligned point cloud in a given region, and the flatness estimation is implemented using the least square method. The proposed method is applied to the online flatness detection of the automotive battery terminal plate. It is compared with the methods based on the ICP algorithm and the threecoordinate measuring instrument. Experimental results show that the proposed method can save 62% of the time, which also has consistent detection results with two methods. As a simple, efficient and reliable technique, the proposed method has broad application prospects and reference value in the field of precision parts manufacturing.