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Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate self-calibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes.

An ultrasensitive dual-signal ratiometric electrochemical sensor was developed for creatinine detection utilizing silver nanoparticles (Ag) with intrinsic self-calibration afforded by iron-nickel bimetallic Prussian blue (FeNiPBA) analogues. The Ag@FeNiPBA exhibits two redox signals corresponding to the Ag+/Ag and Fe3+/Fe2+ systems. Adding chloride (Cl−) solution increases the anodic current of the Ag/Ag system significantly due to the formation of silver chloride through solid-state electrochemistry. While the anodic current of the Ag/Ag system decreases in the presence of creatinine due to the competitive reaction, the Fe/Fe system's anodic current remains the same, which enables a ratiometric response. Under optimized conditions, the response ratio (IAg/IFe) decreases while the creatinine concentration increases linearly between 0.015 and 140 μM, with 0.004 μM as a good detection limit (S/N = 3). These results demonstrate superior performance over previously reported methods for electrochemical creatinine determination. The high sensitivity arises from the signal amplification of the Ag/AgCl solid-state electrochemistry, while the selectivity originates from the specific interaction between Ag+ and creatinine. The Ag@FeNiPBA hybrid can quantify creatinine in real samples with good recoveries. This work opens up new opportunities for applying dual-signal nanostructures to develop electrochemical sensors for (bio)molecule detection.

A new method based on the interferometric pseudo-lateral-shearing method is proposed to evaluate the pitch variation of a large-scale planar variable-line-spacing (VLS) grating. In the method, wavefronts of the first-order diffracted beams from a planar VLS grating are measured by a commercial Fizeau form interferometer. By utilizing the differential wavefront of the first-order diffracted beam before and after the small lateral shift of the VLS grating, the pitch variation of the VLS grating can be evaluated. Meanwhile, additional positioning errors of the grating in the lateral shifting process could degrade the measurement accuracy of the pitch variation. To address the issue, the technique referred to as the reference plane technique is also introduced, where the least squares planes in the wavefronts of the first-order diffracted beams are employed to reduce the influences of the additional positioning errors of the VLS grating. The proposed method can also reduce the influence of the out-of-flatness of the reference flat in the Fizeau interferometer by taking the difference between the measured positive and negative diffracted wavefronts; namely, self-calibration can be accomplished. After the theoretical analysis and simulations, experiments are carried out with a large-scale VLS grating to verify the feasibility of the proposed methods. Furthermore, the evaluated VLS parameters are verified by comparing them with the readout signal of an absolute surface encoder employing the evaluated VLS grating as the scale for measurement.

The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.

The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. If vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system, an accurate calibration of the multi-camera system is a necessary prerequisite. We propose a novel vSLAM-based self-calibration method for a multi-camera system that includes at least one calibrated stereo camera, and an arbitrary number of monocular cameras. We assume overlapping fields of view to only exist within stereo cameras. Our self-calibration estimates the inter-camera transforms with metric scale; metric scale is inferred from calibrated stereo. On our MAV, we set up each camera pair in a stereo configuration which facilitates the estimation of the MAV’s pose with metric scale. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses relative rotation angle measurements from a 3-axis gyroscope to recover the relative motion of the MAV with metric scale and from 2D-2D feature correspondences. This relative motion estimation does not involve scene point triangulation. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated real-time on-board vSLAM with loop closures. We show experimental results from simulation experiments, and real-world experiments in both indoor and outdoor environments.

This paper is concerned with the problem of multi-view 3D reconstruction with an un-calibrated micro-lens array based light field camera. To acquire 3D Euclidean reconstruction, existing approaches commonly apply the calibration with a checkerboard and motion estimation from static scenes in two steps. Self-calibration is the process of simultaneously estimating intrinsic and extrinsic parameters directly from un-calibrated light fields without the help of a checkerboard. While the self-calibration technique for conventional (pinhole) camera is well understood, how to extend it to light field camera remains a challenging task. This is primarily due to the ultra-small baseline of the light field camera. We propose an effective self-calibration method for a light field camera for automatic metric reconstruction without a laborious pre-calibration process. In contrast to conventional self-calibration, we show how such a self-calibration method can be made numerically stable, by exploiting the regularity and measurement redundancies unique for the light field camera. The proposed method is built upon the derivation of a novel ray-space homography constraint (RSHC) using Plücker parameterization as well as a ray-space infinity homography (RSIH). We also propose a new concept of “rays of the absolute conic (RAC)” defined as a special quadric in 5D projective space P5. A set of new equations are established and solved for self-calibration and 3D metric reconstruction specifically designed for a light field camera . We validate the efficacy of the proposed method on both synthetic and real light fields, and have obtained superior results in both accuracy and robustness.

The fish-eye lens camera has a wide field of view that makes it effective for various applications and sensor systems. However, it incurs strong geometric distortion in the image due to compressive recording of the outer part of the image. Such distortion must be interpreted accurately through a self-calibration procedure. This paper proposes a new type of test-bed (the AV-type test-bed) that can effect a balanced distribution of image points and a low level of correlation between orientation parameters. The effectiveness of the proposed test-bed in the process of camera self-calibration was verified through the analysis of experimental results from both a simulation and real datasets. In the simulation experiments, the self-calibration procedures were performed using the proposed test-bed, four different projection models, and five different datasets. For all of the cases, the Root Mean Square residuals (RMS-residuals) of the experiments were lower than one-half pixel. The real experiments, meanwhile, were carried out using two different cameras and five different datasets. These results showed high levels of calibration accuracy (i.e., lower than the minimum value of RMS-residuals: 0.39 pixels). Based on the above analyses, we were able to verify the effectiveness of the proposed AV-type test-bed in the process of camera self-calibration.

Light pens for 3D vision coordinate measurement systems are increasingly widely used due to their advantages, such as their small size, convenience of being carried, and widespread applicability. The posture of the light pen is an important factor that affects accuracy. The pose domain of the pen needs to be given so that the measurement system has a suitable measurement range to obtain more qualified parameters. The advantage of the self-calibration method is that the entire self-calibration process can be completed at the measurement site with no auxiliary equipment. After the system camera calibration was completed, we took several pictures of the same measurement point with different poses to obtain the conversion matrix of the picture and subsequently used spherical fitting, the generalized inverse method of least squares, and the principle of position invariance in the pose domain range. The combined stylus tip center self-calibration method calculates the actual position of the light pen probe. The experimental results verify the effectiveness of the method; the measurement accuracy of the system can satisfy basic industrial measurement requirements.

A low power clock generator with self-calibration for UHF RFID tags compatible with the EPCglobal Class-1 Gen-2 (EPC Gen2) standard is presented. By utilizing the timing information of the reader to tag (R ⇒ T) link symbols, the frequency accuracy of the calibrated clock can meet the stringent requirement of the standard. Designed in a 0.18 μ m standard CMOS technology, simulation results show that the frequency error of the clock source is only from − 2.07 to +  1.4% of the target frequency of 2.56 MHz. The power consumption is only 720 nW.

As rigid robots suffer from the higher inertia of their rigid links, cable-driven parallel robots (CDPRs) are more suitable for large-scale three-dimensional (3D) printing tasks due to their outstanding reconfigurability, high load-to-weight ratio, and extensive workspace. In this paper, a parallel 3D printing robot is proposed, comprising three pairs of driving cables to control the platform motion and three pairs of redundant cables to adjust the cable tension. To improve the motion accuracy of the moving platform, the static kinematic error model is established, and the error sensitivity coefficient is determined to reduce the dimensionality of the optimization function. Subsequently, the self-calibration positions are determined based on the maximum cable length error in the reachable workspace. A self-calibration method is proposed based on the genetic algorithm to solve the kinematic parameter deviations. Additionally, the dynamic errors are effectively reduced by compensating for the elastic deformation errors of the cable lengths. Furthermore, an experimental prototype is developed. The results of dynamic error compensation after the self-calibration indicate a 67.4% reduction in terms of the maximum error along the Z-axis direction. Finally, the developed prototype and proposed calibration and compensation methods are validated through the printing experiment.