Line sensors (1D) are often used for quality monitoring of moving objects in industrial environments. They are, for instance, used to derive dimensional and geometrical information of moving objects or products. Their high sampling rate makes them well suited for retrieving information of fast moving objects. However, in fast motion the 1D sensor, as any other kind of image sensor, introduces artefacts commonly referred to as motion blur. In this paper, we discuss (1D) sensor motion blur and methods to compensate for it. An experimental set-up and a simulation tool have been developed to characterize motion blur of (1D) sensors. Once properly characterized, a deblurring algorithm (based on a non-blind deconvolution method) has been developed to reconstruct a deblurred image. The results are validated using experimental data collected from a vibrating string. Comparison between dimensional feature measurements of the vibrating string, without and with deblurring methods are illustrated. The analysis shows that a decrease by a factor of two on the measurement variance can be achieved by applying the proposed deblurring method.
We have developed an automatic mitigation method for compensating drifts occurring in low-cost Inertial Measurement Units (IMU), using MEMS (Microelectromechanical systems) accelerometers and gyros, and applied the method for online trajectory estimation of a moving robot arm. The method is based on an automatic detection of system’s states which triggers an online (i.e. automatic) recalibration of the sensors parameters. Stationary tests have proven an absolute reduction of drift, mainly due to random walk noise at ambient conditions, up to ~50% by using the recalibrated sensor parameters instead of using the nominal parameters obtained from sensor’s datasheet. The proposed calibration methodology works online without needing manual interventions and adaptively compensates drifts under different working conditions. Notably, the proposed method requires neither any information from an aiding sensor nor a priori knowledge about system’s model and/or constraints. It is experimentally shown in this paper that the method improves online trajectory estimations of the robot using a low-cost IMU consisting of MEMS-based accelerometer and gyroscope. Applications of the proposed method cover automotive, machinery and robotics industries.