| 1. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
- 2021
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Ingår i: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). - : IEEE. - 9781665417143 - 9781665417150 ; , s. 5462-5469
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Konferensbidrag (refereegranskat)abstract
- This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.
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| 2. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
- 2023
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Ingår i: IEEE Transactions on robotics. - : IEEE. - 1552-3098 .- 1941-0468. ; 39:2, s. 1476-1495
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Tidskriftsartikel (refereegranskat)abstract
- This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.
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| 3. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Oriented surface points for efficient and accurate radar odometry
- 2021
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Konferensbidrag (refereegranskat)abstract
- This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training.
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| 4. |
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| 5. |
- Alhashimi, Anas, et al.
(författare)
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An Improvement in the Observation Model for Monte Carlo Localization
- 2014
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Ingår i: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics. - : SciTePress. - 9789897580406 ; , s. 498-505
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Bokkapitel (refereegranskat)abstract
- Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.
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| 6. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures
- 2018
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Ingår i: 2018 European Control Conference (ECC). - Piscataway, NJ : IEEE. - 9783952426982 - 9781538653036 ; , s. 2447-2453
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.
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| 7. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
- 2021
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%.
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| 8. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Calibrating distance sensors for terrestrial applications without groundtruth information
- 2017
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 17:12, s. 3698-3709
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.
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| 9. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.
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| 10. |
- Alhashimi, Anas, et al.
(författare)
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Change-Point and Model Estimation with Heteroskedastic Noise and Unknown Model Structure
- 2023
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Ingår i: Int. Conf. Control, Decis. Inf. Technol., CoDIT. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350311402 ; , s. 2126-2132
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Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate the problem of modeling time-series as a process generated through (i) switching between several independent sub-models; (ii) where each sub-model has heteroskedastic noise, and (iii) a polynomial bias, describing nonlinear dependency on system input. First, we propose a generic nonlinear and heteroskedastic statistical model for the process. Then, we design Maximum Likelihood (ML) parameters estimation method capable of handling heteroscedasticity and exploiting constraints on model structure. We investigate solving the intractable ML optimization using population-based stochastic numerical methods. We then find possible model change-points that maximize the likelihood without over-fitting measurement noise. Finally, we verify the usefulness of the proposed technique in a practically relevant case study, the execution-time of odometry estimation for a robot operating radar sensor, and evaluate the different proposed procedures using both simulations and field data.
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| 11. |
- Alhashimi, Anas
(författare)
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Characterization of Neato Lidar
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The Lidars are very useful sensors in many robotic applications. The problem is that the price of these sensors are quite expensive. A cheap version of these sensors is the Neato {Neato Robotics, Inc. https://www.neatorobotics.com/company/} Lidar. In this report we will present different experiments that had been done to characterize this device. Also discuss the possibilities that can be done to improve its performance in the robotics applications.
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| 12. |
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| 13. |
- Alhashimi, Anas, 1978-
(författare)
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Design and implementation of fast three stages SLA battery charger for PLC systems
- 2011
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Ingår i: Journal of Engineering. - Baghdad, Iraq : University of Baghdad, College of Engineering. - 1726-4073 .- 2520-3339. ; 17:3, s. 448-465
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Tidskriftsartikel (refereegranskat)abstract
- New fast sealed lead acid (SLA) battery chargers must be able to charge the fully discharged batteries in a short time. In the same time, the charger must monitor the battery state of health in order to prevent over charge and to extend the battery life time.In this paper a Fast charger was presented to charge SLA batteries in short time and monitor the battery voltage to prevent over charge. The design was implemented practically. And 150 charger of similar type was produced for commercial use. They are now in service in different Mobile base station sites around Baghdad. It can charge a fully discharged 12V, 4.5Ah battery in less than 5 hours. To supply PLC control system on DC power to about 24 hour of continuous operation during main electricity faults.During one and half year of continuous operation three faults have been recorded in the 150 chargers. All of the three cases were because of bad components manufacturing.
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| 14. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors
- 2015
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 15:12, s. 31205-31223
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Tidskriftsartikel (refereegranskat)abstract
- We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
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| 15. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Modeling and Calibrating Triangulation Lidars for Indoor Applications
- 2018
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Ingår i: Informatics in Control, Automation and Robotics. - Cham : Springer. - 9783319550107 - 9783319550114 ; , s. 342-366
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Bokkapitel (refereegranskat)abstract
- We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: (i) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and (ii) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: (i) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and (ii) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non controlled but structured environments where there is a high chance for the sensor to be detecting objects with flat surfaces (e.g., walls). These newly proposed algorithms are thus specially designed and optimized for inferring precisely the angular orientation of the Lidar sensor with respect to the detected object, a feature that is beneficial especially for indoor navigation purposes.
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| 16. |
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| 17. |
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| 18. |
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| 19. |
- Alhashimi, Anas
(författare)
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Project: Quad Rotor
- 2013
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Annan publikation (populärvet., debatt m.m.)
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| 20. |
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| 21. |
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| 22. |
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| 23. |
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| 24. |
- Alhashimi, Anas
(författare)
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Statistical Calibration Algorithms for Lidars
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are becoming increasingly available and capable, are becoming part of everyday life in applications: robots that guide blind or mentally handicapped people, robots that clean large office buildings and department stores, robots that assist people in shopping, recreational activities, etc.Localization, in the sense of understanding accurately one's position in the environment, is a basic building block for performing important tasks. Therefore, there is an interest in having robots to perform autonomously and accurately localization tasks in highly cluttered and dynamically changing environments.To perform localization, robots are required to opportunely combine their sensors measurements, sensors models and environment model. In this thesis we aim at improving the tools that constitute the basis of all the localization techniques, that are the models of these sensors, and the algorithms for processing the raw information from them. More specifically we focus on:- finding advanced statistical models of the measurements returned by common laser scanners (a.k.a. Lidars), starting from both physical considerations and evidence collected with opportune experiments;- improving the statistical algorithms for treating the signals coming from these sensors, and thus propose new estimation and system identification techniques for these devices.In other words, we strive for increasing the accuracy of Lidars through opportune statistical processing tools.The problems that we have to solve, in order to achieve our aims, are multiple. The first one is related to temperature dependency effects: the laser diode characteristics, especially the wave length of the emitted laser and the mechanical alignment of the optics, change non-linearly with temperature. In one of the papers in this thesis we specifically address this problem and propose a model describing the effects of temperature changes in the laser diode; these include, among others, the presence of multi-modal measurement noises. Our contributions then include an algorithm that statistically accounts not only for the bias induced by temperature changes, but also for these multi-modality issues.An other problem that we seek to relieve is an economical one. Improving the Lidar accuracy can be achieved by using accurate but expensive laser diodes and optical lenses. This unfortunately raises the sensor cost, and -- obviously -- low cost robots should not be equipped with very expensive Lidars. On the other hand, cheap Lidars have larger biases and noise variance. In an other contribution we thus precisely targeted the problem of how to improve the performance indexes of inexpensive Lidars by removing their biases and artifacts through opportune statistical manipulations of the raw information coming from the sensor. To achieve this goal it is possible to choose two different ways (that have been both explored):1- use the ground truth to estimate the Lidar model parameters;2- find algorithms that perform simultaneously calibration and estimation without using ground truth information. Using the ground truth is appealing since it may lead to better estimation performance. On the other hand, though, in normal robotic operations the actual ground truth is not available -- indeed ground truths usually require environmental modifications, that are costly. We thus considered how to estimate the Lidar model parameters for both the cases above.In last chapter of this thesis we conclude our findings and propose also our current future research directions.
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| 25. |
- Alhashimi, Anas, 1978-, et al.
(författare)
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Statistical modeling and calibration of triangulation Lidars
- 2016
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Ingår i: ICINCO 2016. - : SciTePress. - 9789897581984 ; , s. 308-317
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Konferensbidrag (refereegranskat)abstract
- We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046
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| 26. |
- Alhashimi, Anas, 1978-
(författare)
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Statistical Sensor Calibration Algorithms
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wearable devices, healthcare systems, industries, and autonomous cars, to name but a few, rely on quantitative measurements for their operations. Measurements require sensors, but sensor readings are corrupted not only by noise but also, in almost all cases, by deviations resulting from the fact that the characteristics of the sensors typically deviate from their ideal characteristics.This thesis presents a set of methodologies to solve the problem of calibrating sensors with statistical estimation algorithms. The methods generally start with an initial statistical sensor modeling phase in which the main objective is to propose meaningful models that are capable of simultaneously explaining recorded evidence and the physical principle for the operation of the sensor. The proposed calibration methods then typically use training datasets to find point estimates of the parameters of these models and to select their structure (particularlyin terms of the model order) using suitable criteria borrowed from the system identification literature. Subsequently, the proposed methods suggest how to process the newly arriving measurements through opportune filtering algorithms that leverage the previously learned models to improve the accuracy and/or precision of the sensor readings.This thesis thus presents a set of statistical sensor models and their corresponding model learning strategies, and it specifically discusses two cases: the first case is when we have a complete training dataset (where “complete” refers to having some ground-truth informationin the training set); the second case is where the training set should be considered incomplete (i.e., not containing information that should be considered ground truth, which implies requiring other sources of information to be used for the calibration process). In doing so, we consider a set of statistical models consisting of both the case where the variance of the measurement error is fixed (i.e., homoskedastic models) and the case where the variance changes with the measured quantity (i.e., heteroskedastic models). We further analyzethe possibility of learning the models using closed-form expressions (for example, when statistically meaningful, Maximum Likelihood (ML) and Weighted Least Squares (WLS) estimation schemes) and the possibility of using numerical techniques such as Expectation Maximization (EM) or Markov chain Monte Carlo (MCMC) methods (when closed-form solutions are not available or problematic from an implementation perspective). We finally discuss the problem formulation using classical (frequentist) and Bayesian frameworks, and we present several field examples where the proposed calibration techniques are applied on sensors typically used in robotics applications (specifically, triangulation Light Detection and Rangings (Lidars) and Time of Flight (ToF) Lidars).
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| 27. |
- Alhashimi, Anas, 1978-
(författare)
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The application of auto regressive spectrum modeling for identification of the intercepted radar signal frequency modulation
- 2012
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Ingår i: Inventi Impact - Telecom. - Bhopal, India : Inventi Journals Pvt.Ltd.. - 2249-1414 .- 2230-8172. ; 2012:3
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Tidskriftsartikel (refereegranskat)abstract
- In the Electronic Warfare receivers, it is important to know the type of modulation of the intercepted Radar signals (MOP modulation on pulse). This information can be very helpful in identifying the type of Radar present and to take the appropriate actions against it. In this paper, a new signal processing method is presented to identify the FM (Frequency Modulation) pattern from the received Radar pulses. The proposed processing method based on Auto Regressive Spectrum Modelling used for digital modulation classification [1]. This model uses the instantaneous frequency and instantaneous bandwidth as obtained from the roots of the autoregressive polynomial. The instantaneous frequency and instantaneous bandwidth together were used to identify the type of modulation in the Radar pulse. Another feature derived from the instantaneous frequency is the frequency rate of change. The frequency rate of change was used to extract the pattern of the frequency change. Results show that this method works properly even for low signal to noise ratios.
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