Automated Precise 3-D Measurement of Featureless Surfaces Assisted by Laser Grids Projection

Precision of vision-based 3D scanning techniques, such as structured light means are usually dependent to scanning area and resolution of the imaging sensors. However, large objects typically require scanning from multiple views, followed by the stitching of point clouds. This process becomes particularly challenging when dealing with featureless surfaces. For instance, existing vehicle inspection methods struggle to handle automobile components with large, smooth, featureless surfaces. Generally, these methods involve either manually placing markers during the scanning process or performing iterative fine registration after robotic scanning. The manual marking method is cumbersome and prone to introduce contact errors, while the smooth surfaces of the components make it difficult to extract the distinct features needed for fine registration. Both methods have limitations when it comes to completely measuring large, featureless surfaces. In response, this paper introduces a novel, high-accuracy point cloud stitching approach for large, featureless objects, employing robotic scanning alongside projected grid lasers. We unveil a corner extraction method for projected grid features that capitalizes on skeleton lines and rotational symmetry. To enhance accuracy, we leverage a shape-based feature selector and a registration method based on Square Root Cubature Kalman Filter for effective noise suppression. Additionally, a global search strategy is employed for final registration, by balancing cost weighted by overlapping areas and registration errors across multiple views. Our comprehensive experiments confirm the accuracy and reliability of the proposed method for on-site inspection of large surfaces devoid of distinct features.