Joint texture and geometry analysis for robotic adaptive visual inspection

Visual inspection involves dimensional inspection and surface inspection. Dimensional inspection is usually achieved via comparing the scanned model of the inspection target with the CAD model to detect dimensional defects such as bending and missing material. Surface inspection is usually based on textures via image processing and analysis to detect defects such as pitting, crack, scratch, and corrosion. Existing systems used fixed predefined viewpoints to capture geometry and texture from the object. However, defects may appear at different locations, distinguishable defect features may not be captured by the fixed viewpoints. Thus, dynamical planning of the viewpoints is desired to provide better sensing parameters adaptive to actual local geometry, appearance and lighting conditions. This paper proposes a new robotic sensing framework for adaptive visual inspection. Viewpoints are planned based on individual object adaptive to different defect locations. Sensor parameters at each viewpoints are adaptive to the surface conditions for optimized geometry/image quality. Data from all viewpoints are fused together to provide an adaptive model.