Orientation Estimation Research Articles

Accurate Joint Estimation of Position and Orientation Based on Angle of Arrival and Two-Way Ranging of Ultra-Wideband Technology

In wireless sensor networks (WSNs), ultra-wideband (UWB) technology is essential for robot localization systems, especially for methods of the simultaneous estimation of position and orientation. However, current approaches frequently depend on rigid body models, which require multiple base stations and lead to substantial equipment costs. This paper presents a cost-effective UWB SL model utilizing the angle of arrival (AOA) and double-sided two-way ranging (DS-TWR). To improve localization accuracy, we propose a self-localization algorithm based on constrained weighted least squares (SL-CWLS), integrating a weighted matrix derived from a measured noise model. Additionally, we derive the constrained Cramér–Rao lower bound (CCRLB) to analyze the performance of the proposed algorithm. Simulation results indicate that the proposed method achieves high estimation accuracy, while real-world experiments validate the simulation results.

Symmetry-induced ambiguity in orientation estimation from RGB images

The estimation of object orientation from RGB images is a core component in many modern computer vision pipelines. Traditional techniques mostly predict a single orientation per image, learning a one-to-one mapping between images and rotations. However, when objects exhibit rotational symmetries, they can appear identical from multiple viewpoints. This induces ambiguity in the estimation problem, making images map to rotations in a one-to-many fashion. In this paper, we explore several ways of addressing this problem. In doing so, we specifically consider algorithms that can map an image to a range of multiple rotation estimates, accounting for symmetry-induced ambiguity. Our contributions are threefold. Firstly, we create a data set with annotated symmetry information that covers symmetries induced through self-occlusion. Secondly, we compare and evaluate various learning strategies for multiple-hypothesis prediction models applied to orientation estimation. Finally, we propose to model orientation estimation as a binary classification problem. To this end, based on existing work from the field of shape reconstruction, we design a neural network that can be sampled to reconstruct the full range of ambiguous rotations for a given image. Quantitative evaluation on our annotated data set demonstrates its performance and motivates our design choices.

Precision Quaternion Estimation with Partially Norm-Constrained Unscented Kalman Filtering

Abstract Accurate orientation estimation is a key challenge in dynamics and control, particularly for rigid body motion. Quaternions are commonly used to represent rotations due to their computational efficiency, but they must always maintain a unit norm to function correctly. If this constraint is not enforced properly, it can lead to significant errors in orientation estimation. This paper proposes an unscented Kalman filter that ensures the quaternion parameters, a subvector within the state vector, adhere to the unit norm constraint. The proposed method provides a closed-form solution without dividing the state vector into constrained and unconstrained vectors. In simulation studies under high noise conditions, the filter demonstrates significantly improved performance compared to standard unscented and pseudo-unscented Kalman filters, enhancing accuracy during both transient and steady-state phases. These results highlight the importance of enforcing quaternion constraints to reduce mean square error and improve convergence.

Pedestrian POSE estimation using multi-branched deep learning pose net.

In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention on another side. It is occasionally desirable to analyze such orientation estimates using computer-vision tools for automated analysis of pedestrian behavior and intention. This article uses a deep-learning method to demonstrate the pedestrian full-body pose estimation approach. A deep-learning-based pre-trained supervised model multi-branched deep learning pose net (MBDLP-Net) is proposed for estimation and classification. For full-body pose and orientation estimation, three independent datasets, an extensive dataset for body orientation (BDBO), PKU-Reid, and TUD Multiview Pedestrians, are used. Independently, the proposed technique is trained on dataset CIFAR-100 with 100 classes. The proposed approach is meticulously tested using publicly accessible BDBO, PKU-Reid, and TUD datasets. The results show that the mean accuracy for full-body pose estimation with BDBO and PKU-Reid is 0.95%, and with TUD multiview pedestrians is 0.97%. The performance results show that the proposed technique efficiently distinguishes full-body poses and orientations in various configurations. The efficacy of the provided approach is compared with existing pretrained, robust, and state-of-the-art methodologies, providing a comprehensive understanding of its advantages.

Precise Orientation Estimation for Rotated Object Detection Based on a Unit Vector Coding Approach

Existing rotated object detection methods usually use angular parameters to represent the object orientation. However, due to the symmetry and periodicity of these angular parameters, a well-known boundary discontinuity problem often results. More specifically, when the object orientation angle approaches the periodic boundary, the predicted angle may change rapidly and adversely affect model training. To address this problem, this paper introduces a new method that can effectively solve the boundary discontinuity problem related to angle parameters in rotated object detection. Our approach involves a novel vector-based encoding and decoding technique for angular parameters, and a cosine distance loss function for angular accuracy evaluation. By utilizing the characteristics of unit vectors and cosine similarity functions, our method parameterizes the orientation angle as components of the unit vector during the encoding process and redefines the orientation angle prediction task as a vector prediction problem, effectively avoiding the boundary discontinuity problem. The proposed method achieved a mean average precision (mAP) of 87.48% and an average cosine similarity (CS) of 0.997 on the MVTec test set. It also achieved an mAP score of 90.54% on the HRSC2016 test set, which is better than several existing state-of-the-art methods and proves its accuracy and effectiveness.

Testing Bayesian inference of GRMHD model parameters from VLBI data

ABSTRACT Recent observations by the Event Horizon Telescope (EHT) of supermassive black holes M87* and Sgr A* offer valuable insights into their space–time properties and astrophysical conditions. Utilizing a library of model images ($\sim 2$ million for Sgr A*) generated from general-relativistic magnetohydrodynamic (GRMHD) simulations, limited and coarse insights on key parameters such as black hole spin, magnetic flux, inclination angle, and electron temperature were gained. The image orientation and black hole mass estimates were obtained via a scoring and an approximate rescaling procedure. Lifting such approximations, probing the space of parameters continuously, and extending the parameter space of theoretical models is both desirable and computationally prohibitive with existing methods. To address this, we introduce a new Bayesian scheme that adaptively explores the parameter space of ray-traced, GRMHD models. The general relativistic radiative transfer code IPOLE is integrated with the EHT parameter estimation tool THEMIS. The pipeline produces a ray-traced model image from GRMHD data, computes predictions for very long baseline interferometric (VLBI) observables from the image for a specific VLBI array configuration and compares to data, thereby sampling the likelihood surface via a Markov chain Monte Carlo scheme. At this stage we focus on four parameters: accretion rate, electron thermodynamics, inclination, and source position angle. Our scheme faithfully recovers parameters from simulated VLBI data and accommodates time-variability via an inflated error budget. We highlight the impact of intrinsic variability on model fitting approaches. This work facilitates more informed inferences from GRMHD simulations and enables expansion of the model parameter space in a statistically robust and computationally efficient manner.

A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.

Impact of conflicts between long- and short-term priors on the weighted prior integration in visual perception

The prior distribution of values for a specific feature can be categorized as long- or short-term priors based on their respective learning durations. Studies have demonstrated that the visual system can integrate both priors through weighted averaging and then utilize the integrated prior to efficiently encode stimuli. It is unclear what determines the two priors' relative weights. To address this question, we arranged the orientations according to three distributions: natural, anti-natural, and natural with increased-amplitude distributions. The natural distribution mirrors the distribution of orientations in the natural world, so it does not conflict with the long-term prior; according to the Kullback-Leibler divergence analysis, the natural distribution had a higher conflict with the anti-natural distribution than with the natural distribution with increased amplitude. It was found that the cardinal bias — the orientation estimates are biased away from the cardinal orientations — was strongest in the natural distribution with increased amplitude but weakest in the anti-natural distribution. These results were accurately predicted by an efficient Bayesian observer model in which the prior is the weighted integration of the long- and short-term priors. Importantly, the weight of the short-term prior in the new prior decreased as the level of the conflict between the long- and short-term priors increased. Therefore, this study reveals that the visual system integrates the long- and short-term priors through weighted averaging, with the conflicting level between the two priors determining their relative weights in the integration prior. The integrated prior was used by visual systems to efficiently encode stimuli.

Pose estimation of bolster spring based on projection roundness and genetic algorithm in narrow space

Precisely identifying the pose of the bolster spring is vital for its effective disengagement. Addressing the challenge of detecting various positional arrangements of the bolster spring within the constrained space of a bogie frame, a method of estimating the pose of bolster spring based on projection roundness and genetic algorithm is proposed. This approach not only reduces the computational complexity, but also improves the precision of pose estimation. Firstly, Statistical filtering and Random Sample Consensus (RANSAC) are employed for the segmentation of the external spring point cloud and for preliminary orientation estimation. Subsequently, the convergence performance of the genetic algorithm is enhanced through optimization of the selection operator, implementation of an adaptive genetic probability adjustment strategy, and refinement of the encoding method. Finally, an evaluation function based on the projection roundness of the external spring is developed and integrated with an improved adaptive genetic algorithm (IAGA) to achieve precise pose estimation of the bolster spring. Experimental results indicate that the proposed method significantly improves pose estimation accuracy compared to traditional approaches, with an additional time cost of less than 1.5 s. The method achieves a RMSE of 1.71 mm for position and 0.84° for orientation, providing an efficient and accurate pose estimation solution for engineering applications.

A New 3D Mapping Method of Buried Pipelines from GPR B-scans

Abstract Ground penetrating radar (GPR) is a non-destructive testing method originally developed for the investigation of underground space and is now widely used in pipeline detection. To map the underground pipelines using GPR, the main challenges include the estimation of depth and orientation through two-dimensional profiles. In this paper, a new 3D mapping framework of buried pipelines from GPR B-scans is proposed. After obtaining sufficient B-scans through parallel survey lines, the proposed procedure mainly includes four stages: data pre-processing, wave velocity estimation, 2D migration and binarization, and 3D stacked image display. Canny edge detection is used to extract hyperbolic features and estimate the propagation velocity of electromagnetic waves in underground media. Migration and binarization are conducted on pre-processed B-scans before 3D stacking. The results show that the proposed method can accurately and intuitively display the position and direction of pipelines by generating three-dimensional images.

Keypoint-Based Bee Orientation Estimation and Ramp Detection at the Hive Entrance for Bee Behavior Identification System

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in 1024×576 px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on 640×384 px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications.

Research on underwater target signal orientation estimation based on smoothness priors approach

PurposeThis paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones.Design/methodology/approachInitially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer.FindingsSimulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation.Originality/valueTo the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift.

High-resolution motion compensation for brain PET imaging using real-time electromagnetic motion tracking.

Substantial improvements in spatial resolution in brain positron emission tomography (PET) scanners have greatly reduced partial volume effect, making head movement the main source of image blur. To achieve high-resolution PET neuroimaging, precise real-time estimation of both head position and orientation is essential for accurate motion compensation. A high-resolution electromagnetic motion tracking (EMMT) system with an event-by-event motion correction is developed for PET-CTscanners. EMMT is comprised of a source, an array of sensors, and a readout electronic unit (REU). The source acts as a transmitter and emits an EM dipole field. It is placed in close proximity to the sensor array and detects changes in EM flux density due to sensor movement. The REU digitizes signals from each sensor and captures precise rotational and translational movements in real time. Tracked motion in the EMMT coordinate system is synchronized with the PET list-mode data and transformed into the scanner coordinate system by locating paired positions in both systems. The optimal rigid motion is estimated using singular value decomposition. The rigid motion and depth-of-interaction (DOI) parallax effect are corrected by event-by-event rebinning of mispositioned lines-of-response (LORs). We integrated the EMMT with our recently developed ultra-high resolution Prism-PET prototype brain scanner and a commercial Siemens Biograph mCT PET-CT scanner. We assessed the imaging performance of the Prism-PET/EMMT system using multi-frame motion of point sources and phantoms. The mCT/EMMT system was validated using a set of point sources attached to both a mannequin head and a human volunteer, for simulating multiframe and continuous motions, respectively. Additionally, a human subject for [18F]MK6240 PET imaging wasincluded. The tracking accuracy of the Prism-PET/EMMT system was quantified as a root-mean-square (RMS) error of 0.49 for 100 axial rotations, and an RMS error of 0.15 mm for 100 mm translations.The percent difference (%diff) in average full width at half maximum (FWHM) of point source between motion-corrected and static images, within a motion range of and 10 mm from the center of the scanner's field-of-view (FOV), was 3.9%. The measured recovery coefficients of the 2.5-mm diameter sphere in the activity-filled partial volume correction phantom were 23.9%, 70.8%, and 74.0% for the phantom with multi-frame motion, with motion and motion compensation, and without motion, respectively. In the mCT/EMMT system, the %diff in average FWHM of point sources between motion-corrected and static images, within a motion range of and 10 mm from the center of the FOV, was 14%. Applying motion correction to the [18F]MK6240 PET imaging reduced the motion-induced spill-in artifact in the lateral ventricle region, lowering its standardized uptake value ratio (SUVR) from 0.70 to 0.34. The proposed EMMT system is a cost-effective, high frame-rate, and none-line-of-sight alternative to infrared camera-based tracking systems and is capable of achieving high rotational and translational tracking accuracies for mitigating motion-induced blur in high-resolution brain dedicated PETscanners.

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved iterative closest point method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

Super-resolution localization and orientation estimation of multiple dipole sound sources: From a maximum likelihood framework to wind tunnel validation

This paper investigates the identification of multiple dipole sound sources using sound pressures measured from a microphone array. The problem is addressed in the maximum likelihood (ML) framework, where the locations, orientations, and powers of multiple dipole sound sources are unknown parameters to be estimated. By the consistency property of ML, the estimated parameters converge to their actual values, which implies an asymptotically perfect spatial resolution, if a sufficiently high signal-to-noise ratio can be achieved. In order to reduce the dimension of the optimization problem of ML, the contribution of each dipole source to the measured pressures is assumed to be a latent variable and the ML problem is equivalently solved via the expectation–maximization (EM) algorithm, which iteratively and sequentially updates each source contribution and the associated sound source parameters. The number of sound sources can also be determined by the model selection approaches which add a penalty of model dimension to the ML objective function. The proposed method is assessed via a laboratory experiment where the sound field is produced by dipole speakers and a wind tunnel experiment where airframe aerodynamic noise is generated at a high Reynolds number. Experimental results show that the proposed method outperforms existing approaches in the sense of higher spatial resolution, more accurate localization, and the capacity to identify the orientations of multiple dipole sound sources.

Comparison of inertial-based filters for orientation estimation in indoor and outdoor running

Comparison of inertial-based filters for orientation estimation in indoor and outdoor running

Noise reverses the oblique effect: A horizontal effect in orientation estimation and subjective uncertainty

Noise reverses the oblique effect: A horizontal effect in orientation estimation and subjective uncertainty

Probing bidirectional serial dependence in an N-back orientation estimation task

Probing bidirectional serial dependence in an N-back orientation estimation task

Navigating in Light: Precise Indoor Positioning Using Trilateration and Angular Diversity in a Semi-Spherical Photodiode Array with Visible Light Communication

This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver’s position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system’s potential for significant indoor positioning accuracy and reliability improvements.