Initial Position Estimation Research Articles

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Characterization of Candidate Solutions for X-Ray Pulsar Navigation

X-ray pulsar based navigation (XNAV) can be used to estimate spacecraft state information based on observation of distant pulsars. XNAV system concepts typically require an initial position estimate since there are many candidate positions which may produce the same measurement. If position ambiguity can be resolved, XNAV may enable full state estimation without prior information. This study seeks to efficiently find candidate spacecraft positions and determine how to select pulsars to minimize the number of candidate solutions within a given domain. A numeric scheme for determining candidate positions is developed for an arbitrary number of observed pulsars. Pulsar selection criteria are developed in terms of relative direction, period, and phase accuracy. Results indicate that it is more time efficient to observe additional pulsars rather than improve the accuracy of pulsar measurements. A smaller angular separation and increased period of the observed pulsars reduces the number of candidate solutions within a given domain. Phase measurement accuracy should be improved simultaneously for all pulsars rather than prioritizing a single pulsar. These selection criteria are verified by evaluating the number of candidate solutions for permutations of 34 real pulsars.

Estimation of pop-up satellite archival tag initial surface position: applications for eastern Bering Sea crab research

BackgroundClimate change is reshaping Bering Sea crab distributions and recent population declines have elevated the urgency in understanding spatial dynamics in relation to management boundaries. While pop-up satellite archival tags (PSATs) can provide fishery-independent movement information, a high level of spatial resolution is needed to evaluate small-scale (i.e., 10 s of km) movements of crabs. Because PSATs drift at the surface prior to acquisition of a satellite-estimated location (via Argos), the accuracy of pop-up location (i.e., animal terminal position) estimates depends on the ability to accurately estimate drift error. We deployed PSATs (n = 36) on fixed-position moorings in Bristol Bay and Marmot Bay, Alaska to validate a new method to estimate tag pop-up location and an associated error ellipse that uses in situ drift data from surfaced tags to estimate drift error. Estimated pop-up location was compared to the location of tag surfacing (i.e., the tag’s fixed position) and to an alternative estimate (i.e., an early satellite-estimated location). Additional tags were deployed on mature male red king crab (Paralithodes camtschaticus) in Bristol Bay during 2020 (n = 84) and 2021 (n = 90) to compare estimates of tag drift error and crab displacement derived using the method.ResultsFor red king crab tags with pop-up location estimates in 2020 (n = 79) and 2021 (n = 46), mean drift error was 9% and 44% of mean crab displacement, respectively. For fixed-position PSATs with pop-up location estimates (n = 27), mean distance between the estimated pop-up location and the tag’s fixed position was 2.0 km, representing a mean improvement in accuracy of 51% over alternative estimates. Corresponding error ellipse estimates either encompassed the tag’s fixed position (n = 11) or their boundaries were a mean distance of 1.3 km (n = 16) from the fixed position.ConclusionsOur method improves pop-up location estimates for PSAT-tagged animals and is particularly well suited for crabs and other slow-moving benthic marine species. The improved estimates enhance PSAT utility for addressing management and ecological questions that require a fine spatial resolution, such as movement near management boundaries or defining essential habitat for important life history events.

Adaptive-Based Accurate Rotor Initial Position Estimation in Synchronous Reluctance Motors

Adaptive-Based Accurate Rotor Initial Position Estimation in Synchronous Reluctance Motors

Bias Reduced Semidefinite Relaxation Method for 3-D Moving Object Localization Using AOA

This paper addresses the localization of a constant velocity moving object in 3-D using angle-of-arrival (AOA) measurements. Compared with the maximum-likelihood estimator, pseudo-linear approach for AOA localization has a large amount of bias resulting from the model transformation to simplify the solution finding. This work aims at reducing the bias and developing bias-reduced semidefinite relaxation (SDR) methods for estimating the initial position and velocity of the object, in a batch or sequential manner. This is accomplished by first formulating a bias reduced constrained weighted least squares (BR-CWLS) problem from the transformed measurements, through introducing an auxiliary variable and adding a quadratic constraint. Such an intractable non-convex problem is tackled next by applying the SDR technique and relaxing it into a convex semidefinite program (SDP), which is shown to be capable of reaching the solution of the original BR-CWLS problem. For sequential estimation, we formulate a different BR-CWLS problem and utilize SDR for obtaining a sequential estimation method that updates the initial position and velocity estimates at each time step. We conduct the mean square error (MSE) and bias analyses for both estimation methods to assess their expected performance. Simulation results verify the ability of bias reduction and the good performance of the proposed methods.

A High Precision Initial Position Estimation Method for Low Saliency Ratio Machine Based on Image Tracking

In sensorless control of permanent magnet synchronous motor (PMSM) drives, initial rotor position detection is critical to ensure smooth start-up operation. However, extracting initial position information for machines with ultra-low saliency ratios is challenging. To address this issue, this paper proposes an estimation method for rotor position using image tracking based on high-frequency (HF) rotating voltage vector injection. Specifically, this paper extracts positive- and negative-sequence currents in the stationary reference frame and reconstructs the induced current vector. By utilizing the support vector machine (SVM) algorithm, the resulting current vector images, which contain amplitude and phase information of the position, are trained to establish the correlation between the current image and rotor position information. Ultimately, the electric position can be obtained from image recognition. Since this method employs image tracking for position estimation, it accounts for cross saturation and multiple saliencies, leading to the relatively high precision of position estimation. Experimental evaluation shows that the proposed method achieves higher precision compared to conventional observers.

Sensorless initial position estimation strategy for PMa-SynRM drives based on dual rotating high-frequency signal injection

Sensorless initial position estimation strategy for PMa-SynRM drives based on dual rotating high-frequency signal injection

Initial Rotor Position Estimation of SynRM Based on Pulsating Voltage Injection Combined With Finite Position Set Algorithm

Initial rotor position estimation is the basis of stable starting without reverse rotation; also initial position estimation directly affects the maximum starting torque. DC pulse voltage injection is one of the common methods for estimating the initial rotor position, which has low implementation complexity. However, the conventional DC voltage pulse injection methods, which works through injecting three DC voltage pulses, has low accuracy and slow response in estimating initial position. A rotor initial position estimation method based on pulsating voltage injection combined with an iteration algorithm is proposed in this paper. This method works by injecting only one DC voltage pulse to the motor and measuring the peak values of currents and fluxes of the motor; afterwards these values should be applied to an iterative algorithm. The iteration algorithm in the proposed method leads to increment of the accuracy of initial position estimation, and reduction of the number of injected dc pulses in comparison with the conventional methods. The performance of the proposed method is verified experimentally on a synchronous reluctance motor (SynRM). Also, a comparison between the two conventional methods and the proposed method is provided by this SynRM drive experimental setup.

Performance evaluation of coarse alignment methods for autonomous underwater vehicles in mooring conditions

The coarse alignment stage of inertial navigation systems (INS) is a step of paramount importance, specially for vehicles that employ the latter as a reference navigation solution, as is the case of autonomous underwater vehicles (AUVs). For such class of vehicles, the alignment has encumbrances that goes beyond those usually reported for terrestrial and aerial systems, as AUVs generally align at non benign environments (e.g., under swaying and mooring conditions) and have limited auxiliary navigation sensors. This work, hence, proposes a comparative analysis of the most representative coarse alignment methods applied to AUVs in mooring condition. The alignment approach herein explored, throughout its optimized and non-optimized versions, is the attitude decomposition-based initial alignment (ADIA) method, and the proposed comparison procedure tests different types of observation vectors resolved in the frames of interest. Experimental tests are conducted, which are based on kinematic data collected from an unmanned marine vehicle representing the typical swaying and attitude oscillations of AUVs in the mooring state, i.e., when subject to wave induced disturbances. Results lead to practical conclusions that can be applied to the AUV initial attitude estimation and are related to autonomy, accuracy and precision of the methods, as well as to their convergence rate and computational effort.

Magnetometer-Only Kalman Filter Based Algorithms for High Accuracy Spacecraft Attitude Estimation (A Comparative Analysis)

Kalman Filter (KF) based algorithms are the most frequently employed attitude estimation algorithms. Typically, a fully observable system necessitates the use of two distinct sensor types. Therefore, relying on a single sensor, such as a magnetometer, for spacecraft attitude estimation is deemed to be a challenge. The present investigation centers on utilizing magnetometers as the exclusive sensor. Several KF based estimation algorithms have been designed and evaluated to give the designer of spacecraft Attitude and Orbit Control System (AOCS) the choice of a suitable algorithm for his mission based on quantitative measures. These algorithms are capable of effectively addressing nonlinearity in both process and measurement models. The algorithms under examination encompass the Extended Kalman Filter (EKF), Sequential Extended Kalman Filter (SEKF), Pseudo Linear Kalman Filter (PSELIKA), Unscented Kalman Filter (USKF), and Derivative Free Extended Kalman Filter (DFEKF). The comparison of the distinct algorithms hinges on key performance metrics, such as estimation error for each axis, computation time, and convergence rate. The resulting algorithms provide numerous benefits, such as diverse levels of high estimation accuracy (with estimation errors ranging from 0.014o to 0.14o), varying computational demands (execution time ranges from 0.0536s to 0.0584s), and the capability to converge despite large initial attitude estimation errors (which reached 170o). These properties render the algorithms appropriate for utilization by spacecraft designers in all operational modes, supplying high-precision attitude estimations better than (0.5o) despite high magnetometer noise levels, which reached (200 nT).

Integrating Sensing Coil Function into Field Winding for Initial Position Estimation of Nonsalient DC Vernier Reluctance Machine

This letter presents a novel mutual inductance detection method by integrating sensing coil function into field winding for the initial position estimation of nonsalient DC-excited vernier reluctance machines (DC-VRM). The key is to reutilize the field winding as a sensing coil by operating the power transistor states, and then the induced currents can be detected from the closed sensing coil when detection pulses are injected into the armature windings. Combined with the established mutual inductance model, reliable position estimation is easily achieved without additional detection circuits. The proposed method has good potential to be applied to a group of nonsalient machines with independent field and armature windings.

Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

In this work, we develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM) -- achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single crystal aluminum. We conclude with a discussion of our methods' impact on future dislocation studies and possible future research avenues.

Performance Analysis of Localization Algorithms for Inspections in 2D and 3D Unstructured Environments Using 3D Laser Sensors and UAVs

One of the most relevant problems related to Unmanned Aerial Vehicle’s (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy.

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Low-Speed Sensorless Control for the Interior Permanent Magnet Synchronous Motors with Sliding Discrete Fourier Transform

An efficient estimation of the rotor position has always been a premise of the reliable operation for the interior permanent magnet synchronous motors (IPMSM), especially for low-speed conditions because of the small back electromotive force (EMF) and low signal-to-noise ratio (SNR). The commonly used observation method, e.g., sliding mode observer (SMO), is suitable for these surface mounted motors and has no great adaptability to the saliency. In this paper, a novel rotor position (including the real-time position and initial position) estimation method was proposed based on the traditional high-frequency signal injection method. Firstly, high-frequency signals were injected to induce the high-frequency current components which contain the rotor position information. Then, the sliding discrete Fourier transform (SDFT) algorithm was used to extract the amplitudes of the induced current components which could be used to get the real-time and initial rotor positions by a proportional integral (PI) regulator and a polarity identification. Lastly, with the established experiments’ platform, the estimation tests of the rotor position at a low speed have been completed to make verification of the effectiveness of the approach studied in this paper.

An Optimization-Based Initial Position Estimation Method for Switched Reluctance Machines

A new method to detect the initial rotor position of switched reluctance machine (SRM) is presented in this article. Unlike most conventional position estimation methods, the proposed method does not need any extra premeasurement and only the data with finite element method (FEM) are required. First, a linear regression model (LRM) is presented to describe the relationship between FEM and measured inductance characteristics. Then, to detect the position, the residual sum of squares of the proposed LRM is considered as an objective function, which is a convex function with rotor position. The rotor position can be estimated by minimizing the objective function with the golden-section search method. Finally, the accuracy of the proposed estimation algorithm is validated by the experimental results on a three-phase 12/8 pole SRM prototype. Compared with the existing position estimation methods, the proposed method has higher accuracy and less measurement effort. The proposed method can serve as a supplement to provide accurate initial position information for incremental position sensors.

Target Localization in Multistatic Systems Without the Bistatic Range Measurement Noise Power

This letter investigates the problem of determining the static target in multistatic systems using bistatic range (BR) measurements, which can be obtained by some local sensor signal processing methods. The localization problem is first formulated as a semidefinite programming problem by using the semidefinite relaxation technique. Then, an initial position estimate is given by augmenting the unknown parameter space, which is required for solving the SDP problem. Specifically, this initialization is also a closed-form solution. Compared with existing algorithms, the proposed estimator has the advantage of not relying on the prior knowledge of BR measurement noise power. Simulation results validate the performance of the proposed estimator.

A Rotor Initial Position Estimation Method for Surface-Mounted Permanent Magnet Synchronous Machine

A new rotor initial position estimation method for surface-mounted permanent magnet synchronous machines (SPMSM) is presented in this paper. The proposed method is based on the magnetic saturation effect and is suitable to prevent starting failure and reverse rotation. Compared with conventional methods, the proposed method shows better effective estimation performance. New strategies for utilizing current responses and voltage pulse injection process are developed. As a result, only three pulses are needed in total at no cost of extra sensors, while maintaining at least 30-degree estimation resolution at the same time. Moreover, a boundary detection strategy (BDS) is proposed to enhance the estimation performance regarding the impact of current measurement noises. Further, based on the BDS, a concept of `boundary width' is proposed. By properly selecting the boundary width, the estimation resolution can be improved from 30 degrees to 15 degrees. Experimental results are given to verify the effectiveness of the proposed method.

Phase Inductance and Rotor Position Estimation for Sensorless Permanent Magnet Synchronous Machine Drives at Standstill

Sensorless control is a common control method used in variable speed drive applications with permanent magnet synchronous machines (PMSMs). In these drive systems, phase inductance (i.e., ${q}$ - and ${d}$ -axis inductance) is the key parameter in designing current controllers. Additionally, the rotor position is essential for coordinate transformation during the motor startup. This paper presents the two offline methods of 1) line-to-line voltage injection and 2) stator qd voltage injection of sensorless PMSM drives for the estimation of phase inductance and initial rotor position, respectively. A distinct feature of the proposed methods is that they estimate both inductance and rotor position using the same algorithm. These methods were experimentally verified using an interior PMSM (IPMSM) and a surface-mounted PMSM (SPMSM). Because of the use of a least-squares algorithm, only a limited number of voltage vectors are required to achieve reasonable accuracy for measuring inductance. However, the accuracy of the estimated position was linearly proportional with the number of voltage vectors injected. Furthermore, the position error for IPMSM was found to be smaller than that for SPMSM.

Sensorless attitude estimation of three-degree-of-freedom actuator for image stabilization

Space recognition is a crucial issue for autonomous systems such as unmanned aerial vehicles, walking robots, and autonomous cars. These systems are necessary to obtain information using a camera while moving. However, reduction in recognition accuracy and system size increase has created problems. To solve these problems, we proposed the three-degree-of-freedom (DOF) actuator for image stabilization. This paper proposes a novel initial position estimation method and sensorless attitude estimation method for the three-DOF actuator to further downsize and reduce weight. The effectiveness of the proposed methods is verified by a magnetic field analysis, using a three-dimensional finite element method and numerical simulations.