Camera calibration for multi-modal robot vision based on image quality assessment

Multi-dimension robot vision in autonomous humanoid robot is still an open issue as it performs less effective when dealing with different environments. Robot vision becomes more challenging as image quality degrades. Unlike human vision, current robot vision is yet to calibrate automatically when image quality changes abruptly. This may result in poor accuracy due to false negative input data points, and the user needs recapturing new calibration images to compensate. Therefore, this study emphasizes on proposing an automatic calibration for multimodal robot vision based on quality measures. We organize our research methodology into three steps. First, we capture a series of image patterns by using our calibration pattern equipment. Second, we employ Image Quality Assessment Function (IQAF) that includes PSNR and SSIM to measure points of image abruption simultaneously. In the experiment, we observed differences between real distance and computed distance and compared them to those of the selfcollected original database and the blur database.