Rotation Angle Estimation Research Articles

High-Precision Positioning and Rotation Angle Estimation for a Target Pallet Based on BeiDou Navigation Satellite System and Vision.

In outdoor unmanned forklift unloading scenarios, pallet detection and localization face challenges posed by uncontrollable lighting conditions. Furthermore, the stacking and close arrangement of pallets also increase the difficulty of positioning a target pallet. To solve these problems, a method for high-precision positioning and rotation angle estimation for a target pallet using the BeiDou Navigation Satellite System (BDS) and vision is proposed. Deep dual-resolution networks (DDRNets) are used to segment the pallet from depth images and RGB images. Then, keypoints for calculating the position and rotation angle are extracted and further combined with the 3D point cloud data to achieve accurate pallet positioning. Constraining the pixel coordinates and depth coordinates of the center point of the pallet and setting the priority of the pallet according to the unloading direction allow the target pallet to be identified from multiple pallets. The positioning of the target pallet in the forklift navigation coordinate system is achieved by integrating BDS positioning data through coordinate transformation. This method is robust in response to lighting influences and can accurately locate the target pallet. The experimental results show that the pallet positioning error is less than 20 mm, and the rotation angle error is less than 0.37°, which meets the accuracy requirements for automated forklift operations.

Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model.

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone's angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation.

High Precision Positioning and Rotation Angle Estimation of a Flatbed Truck Based on BDS and Vision.

Centimeter-level localization and precise rotation angle estimation for flatbed trucks pose significant challenges in unmanned forklift automated loading scenarios. To address this issue, the study proposed a method for high-precision positioning and rotation angle estimation of flatbed trucks using the BeiDou Navigation Satellite System (BDS) and vision technology. First, an unmanned forklift equipped with a Time-of-Flight (ToF) camera and a dual-antenna mobile receiver for BDS positioning collected depth images and localization data near the front and rear endpoints of the flatbed. The Deep Dual-Resolution Network-23-slim (DDRNet-23-slim) model was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform. The algorithm then computed the set of intersection points of the lines. A neighborhood feature vector was designed to identify the endpoint of a flatbed from a set of intersection points using feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BDS positioning. A rotation angle estimation was then performed using the endpoints at the front and rear. Experiments showed that the endpoint positioning error was less than 3 cm, and the rotation angle estimation error was less than 0.3°, which verified the validity and reliability of the method.

UHead

The focus of Advanced driver-assistance systems (ADAS) is extending from the vehicle and road conditions to the driver because the driver's attention is critical to driving safety. Although existing sensor and camera based methods can monitor driver attention, they rely on specialised hardware and environmental conditions. In this paper, we aim to develop an effective and easy-to-use driver attention monitoring system based on UWB radar. We exploit the strong association between head motions and driver attention and propose UHead that infers driver attention by monitoring the direction and angle of the driver's head rotation. The core idea is to extract rotational time-frequency representation from reflected signals and to estimate head rotation angles from complex head reflections. To eliminate the dynamic noise generated by other body parts, UHead leverages the large magnitude and high velocity of head rotation to extract head motion information from the dynamically coupled information. UHead uses a bilinear joint time-frequency representation to avoid the loss of time and frequency resolution caused by windowing of traditional methods. We also design a head structure-based rotation angle estimation algorithm to accurately estimate the rotation angle from the time-varying rotation information of multiple reflection points in the head. Experimental results show that we achieve 12.96° median error of 3D head rotation angle estimation in real vehicle scenes.

A Hybrid Method Combining Data-Driven and Model-Based Algorithms for External Force-Sensing and Haptics Control of Cable-Pulley-Driven Surgical Robotic Manipulator

Abstract The implementation of the high-precision tracking control and external force-sensing ability of a manipulator is important for achieving refined surgical robot operation. In this paper, a hybrid method based on data-driven and model-based algorithms is proposed for the manipulator of a cable-pulley-driven surgical robot. This method integrates an artificial neural network and a dynamic model rotation angle estimation, and a full closed-loop control architecture is further constructed. The algorithm compensates for the hysteresis of the joint angle and effectively improves the tracking control precision. Based on the architecture, the external force estimator (EFE) using a joint torque disturbance observer and the force interaction teleoperation control strategy using a direct force feedback framework (DFF) are implemented. In the force loading experiment, it was shown that the EFE performs well for static and dynamic force estimation, and the teleoperated haptic control experiment showed that the DFF-EFE-based system has a high position-tracking accuracy with real-time external force-sensing ability.

Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation

Deep learning in computer vision is becoming increasingly popular and useful for tracking object movement in many application areas, due to data collection burgeoning from the rise of the Internet of Things (IoT) and Big Data. So far, computer vision has been used in industry predominantly for quality inspection purposes such as surface defect detection; however, an emergent research area is the application for process monitoring involving tracking moving machinery in real time. In steelmaking, the deployment of computer vision for process monitoring is hindered by harsh environments, poor lighting conditions and fume presence. Therefore, application of computer vision remains unplumbed. This paper proposes a novel method for tracking hot metal ladles during pouring in poor lighting. The proposed method uses contrast-limited adaptive histogram equalisation (CLAHE) for contrast enhancement, Mask R-CNN for segmentation prediction and Kalman filters for improving predictions. Pixel-level tracking enables pouring height and rotation angle estimation which are controllable parameters. Flame severity is also estimated to indicate process quality. The method has been validated with real data collected from ladle pours. Currently, no publications presenting a method for tracking ladle pours exist. The model achieved a mean average precision (mAP) of 0.61 by the Microsoft Common Objects in Context (MSCOCO) standard. It measures key process parameters and process quality in processes with high variability, which significantly contributes to process enhancement through root-cause analysis, process optimisation and predictive maintenance. With real-time tracking, predictions could automate ladle controls for closed-loop control to minimise emissions and eliminate variability from human error.

Improved SURF method in digital image correlation for estimation of large rotation angle

Digital image correlation (DIC) is widely used in optical measurement techniques with the advantages of high precision, robustness and expansibility. The conventional DIC, however, is highly dependent on the accuracy of initial parameters, and there are reports [1,2] that it may fail when the rotation angle of the target specimen exceeds 7°. The combination of Speeded-Up Robust Features (SURF) with Random Sample Consensus (RANSAC) algorithm has been applied to solve this problem with limited success due to the effects of residual mismatches. An enhanced algorithm based on SURF is proposed in this study to address this problem. The transformation model between two images is obtained by RANSAC similar to existing methods. Then an angle compensation strategy is proposed to estimate the rotation angle of the deformed image to form the compensated image. Finally, an iterative process is proposed to refine the initial parameters from the reference and deformed images with a new convergence threshold. In addition, an evaluation criterion developed from a multiple ring template is proposed to compute the similarity of feature point pairs to improve the speed of convergence and sampling accuracy. Results from numerical simulation and experimental analysis show that the accuracy and robustness of the proposed method are significantly improved. Further application of the proposed method to monitor the rotating blades of a scaled model wind turbine illustrates further the capability of the proposed method.

Image Large Rotation and Scale Estimation Using the Gabor Filter

This paper proposes a novel image large rotation and scale estimation method based on the Gabor filter and pulse-coupled neural network (PCNN). First, the Gabor features of the template image and its rotated one are extracted by performing the Gabor filter. Second, we present a modified PCNN model to measure the similarity between the Gabor features of the image and its rotated one. Finally, the rotation angle is calculated by searching the global minimum of the correlation coefficients. Besides rotation estimation, we also propose a scale estimation method based on the max-projection strategy. The Gabor feature image is projected along the estimated rotation angle, and the scale is calculated by searching the peak of this projection result. Moreover, experiments illustrate that the proposed method has high accuracy on rotation and scale estimation and is robust to noise. Compared with the state-of-the-art methods, the proposed approach has a more stable performance.

Effects of IMU Sensor-to-Segment Misalignment and Orientation Error on 3-D Knee Joint Angle Estimation

Accurate joint angle estimation using magneto-inertial measurement units (IMU) is a significant challenge for human movement analysis outside of the lab. Sensor-to-segment misalignment and sensor orientation errors are key factors in joint angle estimation. However, the effects of these factors on 3-D knee joint angle estimation accuracy are not well understood. This paper presents a general model to investigate the effects of sensor-to-segment misalignment and IMU orientation error on 3-D knee joint angle estimation accuracy. An analytical model was built to quantify the effects of sensor-to-segment misalignment and IMU orientation errors for 3-D knee joint angle estimation. Precise outcomes were quantified via simulation analysis and experimental testing in which fourteen healthy subjects performed drop landing and cutting tasks while wearing IMUs and optical motion capture markers for ground truth estimation. Sensor-to-segment misalignment accounted for 52% of the total knee abduction angle error and 40% of the knee internal rotation angle error but did not significantly affect knee flexion angle error. Conversely, IMU orientation error accounted for 55% of the knee flexion angle error but did not significantly affect knee abduction or internal rotation angle errors. These results indicate that sensor-to-segment misalignment significantly affects knee abduction and internal rotation angle estimation while IMU orientation error significantly affects knee flexion angle estimation. This study could serve to inform the development of accurate IMU-based 3-D knee joint angle algorithms for human movement analysis.

Rotation Estimation and Segmentation for Patterned Image Vision Inspection

Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern.

Quantum process tomography of a magneto-optic transformation

We characterize a magneto-optic transformation in the Faraday modality via quantum process tomography (QPT) with quantum light comprising of antibunched single photons generated through heralded spontaneous parametric downconversion. We derive a general process matrix for a unitary transformation which effectively describes the action of the magneto-optic element. This matrix quantifies the polarization attributes of the single photon as it traverses through the magneto-optic element. The magnitude of rotation and ellipticity are extracted through a maximum likelihood based optimization of the process matrix. Measurements are performed for a wide range of temperatures (8–100 K) and applied magnetic fields. The estimated rotation angles are curve fit to determine the Curie-Weiss constant for a terbium gallium garnet crystal. The extracted angles are also utilized to map the rotations on the surface of Bloch sphere.

RF-Based Indoor Moving Direction Estimation Using a Single Access Point

Indoor moving direction and rotation angle measurements are crucial to many ubiquitous mobile computing applications. Most of the state-of-the-art approaches rely on inertial sensors, e.g., accelerometers, gyroscopes, and magnetometers, which suffer from severe accumulative errors or accuracy degradation indoors. This article presents an RF-based direction estimation method, which utilizes the off-the-shelf commodity WiFi devices for accurate moving direction and in-place rotation angle estimation. The proposed approach employs a novel 2-D antenna array and leverages the spatial decay property of the time-reversal resonating strength. First, the moving speeds along different directions, specified by the 2-D array, are derived using virtual antenna alignment. The precise estimation of the device’s moving direction is then achieved by combining the obtained velocity information and the a prior knowledge of the array’s geometry layout. Experiments in a multipath-rich indoor environment have shown that the median error for moving direction estimation is 6.9°, which outperforms the accelerometer counterpart. The results also verify the good accuracy of in-place rotation angle estimation without any accumulative error, which beats the gyroscope in long-term tests. Because the proposed approach can achieve high accuracy without accumulative drifts, it is a promising candidate solution to applications that require accurate direction information.

Replacing DWT with DTCWT in blind image rotation angle estimation

During the image tampering, rotation is often involved to make the forgery more convincing. Hence, estimating the rotation angle accurately and locally is of forensic importance. Recently, a novel rotation angle estimation scheme was proposed based on linear pattern (LP), achieving state of the art performance especially in the case when the rotation angle is small. However, due to the limitations of the involved discrete wavelet transform (DWT), the existing LP based method cannot always accurately detect rotated linear pattern from a rotated image. To fill this gap, we propose to extract the rotated LP using dual tree complex wavelet transform (DTCWT). Thanks to the good directional selectivity of DTCWT, the proposed method can extract the LP more accurately. Experiments show that our proposed method performs better than the state of the art in rotation angle estimation, and is also a promising forensic tool for tampering localization.

Rotation-Aware 3D Vehicle Detection From Point Cloud

Three-dimensional vehicle detection using LiDAR point clouds is important for the stability of autonomous driving. It can provide high-quality three-dimensional information for most obstacles. Although efficient algorithms based on a Bird’s Eye View (BEV) feature map have been developed, many research issues still remain; in particular, the existing methods show a limited accuracy in estimating the rotation angle of a 3D object. In this paper, to improve the accuracy of rotation angle estimation, we propose a rotation-aware 3D vehicle detector that extracts distinguishable features from the proposals with various angles of rotations. Experiments are conducted on KITTI dataset and Waymo Open dataset. Our approach improves detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency.

Monocular-Vision and CAD Model based Online Roll-Axis Registration

ABSTRACT This study proposes a single-axis registration system based on monocular vision and CAD modelling without employing three-dimensional scanning or reconstruction techniques. The developed system uses dynamic modelling and image projection methods to estimate the single-axis rotation angle of an inspected workpiece. The estimated rotation angle of the inspected workpiece is achieved using the pose of the virtual model. Dynamic modelling significantly reduces both the data storage and computing time required to match the virtual model. Through experiments, we found that the average root-mean-square (RMS) error between the true centre location of special features within the inspected workpiece and those constructed within the virtual model was 2.19 pixels, and the RMS overlap rate was 96.44%. Thus, a monocular vision three-dimensional workpiece registration system with a dynamic model is effectively realised.

Итерационный алгоритм оценки смещения и угла поворота при влиянии аддитивной и мультипликативной помехи для пространственно-временного совмещения телевизионных сигналов

The estimation of displacements, rotation angle and other deformation of images is an urgent problem in the intelligence video recording system. One of the main tasks in assessing image deformations is to increase the speed of data processing. Significant amounts of information contained in a television signal limit the possibilities of using brute force methods, which are a universal solution for estimating unknown parameters. The paper considers the problem of estimating the displacement and angle of rotation under the influence of additive and multiplicative interference for the spatial-temporal alignment of television signals. Reducing the processing time is carried out through the use of an iterative procedure for estimating parameters by successive approximation with separate estimation of the angle of rotation and displacement.

Sensorless Starting Control of Brushless Synchronous Starter/Generators for the Full-Speed Range

Signal injection degrades the starting performance of the brushless synchronous starter/generator (BSSG), and reversible magnetic saliency of the main generator (MG) leads to failure for most traditional sensorless methods. A novel full-speed sensorless starting control method of BSSG is proposed to solve these problems. The main exciter (ME), mechanically coupled to MG, is used as a position sensor of MG at low speed. The position of ME is extracted from the hexagonal trajectory of the rotor current space vector and then converted into the rotation angle of MG. At standstill, a fast initial position detection of MG is conducted with the electromotive force (EMF) of MG, induced by the establishment of the field current of MG. Then, the continuous position of MG is the sum of the estimated rotation angle and initial position. At high speed, flux weakening of MG is achieved by changing the structure of ME, invalidating the approach dependent on ME. Thus, estimations at low speed are replaced by ones based on extended EMF of MG gradually before the structure change. Signal injection is not needed and effects of reversible magnetic saliency are avoided in this approach. The effectiveness of this method is verified by experiments.

Accurate angle estimation based on moment for multirotation computation imaging.

In a multirotation computation imaging system, the fidelity of the reconstructed result is limited by the accuracy of the estimated rotation angles. Here, an accurate angle detection method using image moment is proposed to estimate angles of diffraction images. The second moment of a digital image is adopted as the rotational inertia in order to estimate angles of diffraction images. Compared with previous versions based on Radon/Hough transform, it has higher accuracy and is simultaneously time-saving, which is verified in both simulation and experiment. The angle error of moment method is narrowed down within 0.1°, or even less, and it also can perform well in sample diversity or when slightly out of focus.

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

Image rotation is a common auxiliary method of image tampering, which can make the forged image more realistic from the geometric perspective. Most algorithms of image rotation angle estimation employ the peak value on the Fourier spectrum; however, JPEG post-processing brings additional peak interferences to the spectrum, which has a great impact on algorithm performance. In this paper, angle estimation is carried out for images compressed by JPEG. Firstly, the Fourier cyclic spectrum of image covariance is calculated, followed by semi-soft threshold wavelet transform to eliminate the block artefacts brought by JPEG compression. According to the shortest distance principle in the range of the limited amplitude, the processed cyclic spectral data are sorted to select the peak points. Finally, according to the selected peak point, the corresponding position coordinates of the theoretical peak point are found, and the rotation angle of the image is estimated by the theoretical peak point. Experimental results demonstrate that the average absolute error of the proposed algorithm is significantly lower than that of the state-of-the-art methods investigated, which highlights the promising potential of the proposed method as an image resampling detector in practical forensics applications.

Quasi single-shot two-pulse tilted pulse front autocorrelator coupled with spatial chirp and spectral divergence monitor for ultrashort pulsed laser beams

Simultaneous single-shot unambiguous detection and measurement of pulse front tilt, spatial chirp, spectral divergence and pulse width has been carried out using two-pulse tilted pulse-front autocorrelator (TP-TPFAC) coupled with spectral interferometry using small part of laser beam intercepted from Mach Zehnder delay unit of TP-TPFAC generating two delayed and spatially inverted pulses. Error in estimation of rotation angle of autocorrelation (AC) trace due to accidental improper positioning of CCD detector is avoided by measuring angle between AC traces corresponding to two delayed and spatially inverted pulses. Likewise, accuracy in estimation of spatial chirp is improved by using spatially resolved spectral trace of two spatially inverted pulses, as errors due to accidental improper positioning of either grating of spectrograph or CCD detector are avoided. Sensitivities and accuracies of the two methods are compared in different experimental situations. Estimated pulse parameters are found in close accordance with respective theoretical values.