Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.

Tightly-coupled fusion of iGPS measurements in optimization-based visual SLAM

The monocular visual Simultaneous Localization and Mapping (SLAM) can achieve accurate and robust pose estimation with excellent perceptual ability. However, accumulated image error over time brings out excessive trajectory drift in a GPS-denied indoor environment lacking global positioning constraints. In this paper, we propose a novel optimization-based SLAM fusing rich visual features and indoor GPS (iGPS) measurements, obtained by workshop Measurement Position System, (wMPS), to tackle the problem of trajectory drift associated with visual SLAM. Here, we first calibrate the spatial shift and temporal offset of two types of sensors using multi-view alignment and pose optimization bundle adjustment (BA) algorithms, respectively. Then, we initialize camera poses and map points in a unified world frame by iGPS-aided monocular initialization and PnP algorithms. Finally, we employ a tightly-coupled fusion of iGPS measurements and visual observations using a pose optimization strategy for high-accuracy global localization and mapping. In experiments, public datasets and self-collected sequences are used to evaluate the performance of our approach. The proposed system improves the result of absolute trajectory error from the current state-of-the-art 19.16mm (ORB-SLAM3) to 5.87mm in the public dataset and from 31.20mm to 5.85mm in the real-world experiment. Furthermore, the proposed system also shows good robustness in the evaluations.

Rotation alignment of a camera-IMU system using a single affine correspondence

We propose an accurate and easy-to-implement method on rotation alignment of a camera-inertial measurement unit (IMU) system using only a single affine correspondence in the minimal case. The known initial rotation angles between the camera and IMU are utilized; thus, the alignment model can be formulated as a polynomial equation system based on homography constraints by expressing the rotation matrix in a first-order approximation. By solving the equation system, we can recover the rotation alignment parameters. Furthermore, more accurate alignment results can be achieved with the joint optimization of multiple stereo image pairs. The proposed method does not require additional auxiliary equipment or a camera's particular motion. The experimental results on synthetic data and two real-world data sets demonstrate that our method is efficient and precise for the camera-IMU system's rotation alignment.