| 1. |
- 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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| 2. |
- 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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| 3. |
- Alhashimi, Anas, 1978-, et al.
(författare)
-
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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| 4. |
- 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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| 5. |
- 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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| 6. |
- Alhashimi, Anas, 1978-, et al.
(författare)
-
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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| 7. |
- 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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| 8. |
- Alzghoul, Ahmad, et al.
(författare)
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Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection : A hydraulic drive system application
- 2014
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Ingår i: Computers in industry (Print). - : Elsevier. - 0166-3615 .- 1872-6194. ; 65:8, s. 1126-1135
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Tidskriftsartikel (refereegranskat)abstract
- The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems.The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability.
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| 9. |
- Alzghoul, Ahmad, et al.
(författare)
-
Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application
- 2014
-
Ingår i: Computers in industry (Print). - : Elsevier BV. - 0166-3615 .- 1872-6194. ; 65:8, s. 1126-1135
-
Tidskriftsartikel (refereegranskat)abstract
- The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods. In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems. The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability
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| 10. |
- Alzghoul, Ahmad, et al.
(författare)
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Data stream forecasting for system fault prediction
- 2012
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 62:4, s. 972-978
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Tidskriftsartikel (refereegranskat)abstract
- Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.
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