Mohammad Saadatseresht

Bio: Mohammad Saadatseresht is an academic researcher from University of Tehran. The author has contributed to research in topics: Photogrammetry & Point cloud. The author has an hindex of 11, co-authored 68 publications receiving 580 citations. Previous affiliations of Mohammad Saadatseresht include University College of Engineering & University of Isfahan.

Spoofing Detection of Civilian UAVs Using Visual Odometry

Abstract: Spoofing of Unmanned Aerial Vehicles (UAV) is generally carried out through spoofing of the UAV’s Global Positioning System (GPS) receiver. This paper presents a vision-based UAV spoofing detection method that utilizes Visual Odometry (VO). This method is independent of the other complementary sensors and any knowledge or archived map and datasets. The proposed method is based on the comparison of relative sub-trajectory of the UAV from VO, with its absolute replica from GPS within a moving window along the flight path. The comparison is done using three dissimilarity measures including (1) Sum of Euclidian Distances between Corresponding Points (SEDCP), (2) angle distance and (3) taxicab distance between the Histogram of Oriented Displacements (HOD) of these sub-trajectories. This method can determine the time and location of UAV spoofing and bounds the drift error of VO. It can be used without any restriction in the usage environment and can be implemented in real-time applications. This method is evaluated on four UAV spoofing scenarios. The results indicate that this method is effective in the detection of UAV spoofing due to the Sophisticated Receiver-Based (SRB) GPS spoofing. This method can detect UAV spoofing in the long-range UAV flights when the changes in UAV flight direction is larger than 3° and in the incremental UAV spoofing with the redirection rate of 1°. Additionally, using SEDCP, the spoofing of the UAV, when there is no redirection and only the velocity of the UAV is changed, can be detected. The results show that SEDCP is more effective in the detection of UAV spoofing and fake GPS positions.

Visibility Analysis In Vision Metrology Network Design

Abstract: The aim of this paper is to present a method whereby accuracy enhancement of an existing photogrammetric network is achieved through the automatic selection of additional camera stations. The determination of the positions of these ‘‘accuracy fulfilment’’ camera stations is based upon what has been termed ‘‘visibility uncertainty prediction modelling’’ of visibility constraints derived from the existing network geometry. Following a review of vision constraints in network design, the concepts of visibility uncertainty prediction and visibility uncertainty spheres are introduced. These provide a mechanism to predict the visibility of current object target points for the new accuracy fulfilment images. This in turn aids in network design improvement. The visibility uncertainty modelling is then illustrated for two close range photogrammetric network configurations, for which the test results demonstrate that the proposed model can reliably predict target visibility with an overall certainty of 75%. Resume On presente dans cet article une methode permettant d'accroitre la precision atteinte dans un reseau photogrammetrique donne, par le choix automatique de stations supplementaires de prises de vues. Pour determiner les positions de ces stations de mise en place des cameras, on se base sur ce qu'on a appele un modele de prevision des risques de non-visibilite, deduit des contraintes de visibilite ressortant de la geometrie du reseau donne. Dans la phase de conception d'un reseau, l'analyse des contraintes de visibilite debouche sur des notions de prevision des risques de non-visibilite et des spheres d'incertitude correspondantes. Il en resulte un mecanisme de prevision de la visibilite des points vises dans l'espace-objet pour les nouvelles images destinees a accroitre la precision photogrammetrique. Ce qui constitue en retour une aide pour ameliorer la conception de ce reseau. On presente enfin une experimentation de cette methode pour deux configurations de reseaux de photogrammetrie a courte distance, ainsi que les resultats obtenus montrant que le modele propose est fiable et prevoit la visibilite des cibles avec une exactitude globale de 75%. Zusammenfassung In diesem Beitrag wird ein Verfahren vorgestellt, um die Genauigkeit einer bestehenden photogrammetrischen Aufnahmekonfiguration durch automatische Wahl von weiteren Kamerastationen zu verbessern. Die Bestimmung der zusatzlichen Positionen erfolgt auf der Basis von Sichtbarkeitsbedingungen, die aus einem bestehenden Netz abgeleitet werden, und wird hier als Abschatzung der Sichtbarkeitsunsicherheit bezeichnet. Nach einer Besprechung der Sichtbarkeitsbedingungen fur das Design von Netzen werden die Konzepte fur eine Vorhersage und Bestimmung der Bereiche fur Sichtbarkeitsunsicherheiten vorgestellt. Beide stellen einen Mechanismus zur Verfugung, um die Sichtbarkeit von bestehenden Objektpunkten in den neuen Bildern vorherzusagen, was wiederum zu einem verbesserten Netzwerkdesign fuhrt. Das vorgeschlagene Verfahren wird an zwei Aufnahmekonfigurationen von Nahbereichsanwendungen demonstriert, bei denen die Testergebnisse zeigen, dass eine zuverlassige Vorhersage der Sichtbarkeit von Objektpunkten mit einer Genauigkeit von ca. 75% moglich ist. Resumen El objetivo de este articulo es presentar un metodo con el que se logra una mejora de la exactitud de una red fotogrametrica existente mediante la seleccion automatica de emplazamientos de toma adicionales. La determinacion de las posiciones de unos emplazamientos que cumplan los requerimientos de exactitud se basa en lo que hemos denominado ‘modelado predictivo de la incertidumbre de visibilidad’ aplicado a las restricciones de la misma derivadas de la geometria de la red existente. Como resultado del estudio de las limitaciones de visibilidad en el diseno de la red, se introducen los conceptos de prediccion de la incertidumbre de la visibilidad y de esferas de incertidumbre de la visibilidad. Dichos conceptos proporcionan un mecanismo para predecir la visibilidad de los blancos existentes para las nuevas imagenes que cumplan los requerimientos de exactitud. A su vez, esto ayuda a mejorar el diseno de la red. El concepto de modelado de la incertidumbre de la visibilidad se ilustra posteriormente con dos configuraciones de red fotogrametrica terrestre, para las que los resultados del ensayo demuestran que el modelo propuesto puede predecir con fiabilidad la visibilidad de los blancos con una certidumbre global de un 75%.

Exponential fringe pattern projection approach to gamma-independent phase computation without calibration for gamma nonlinearity in 3D optical metrology.

Abstract: This paper presents a method that expresses the fringe pattern as an exponential function and a mathematical model for gamma-independent phase computation. The method was compared to: (i) conventional phase measurement without nonlinearity correction, and (ii) conventional gamma correction by pattern pre-distortion based on an input-to-projector camera-output look-up table. The pre-distorted and exponential methods achieved large reduction in error compared to conventional computation with no gamma correction. The advantage of the exponential method is that no system gamma nonlinearity calibration procedure or information is required. This reduces optical system setup before measurement and permits easier use of off-the-shelf projectors.

Range Camera Self-Calibration Based on Integrated Bundle Adjustment via Joint Setup with a 2D Digital Camera

Abstract: Time-of-flight cameras, based on Photonic Mixer Device (PMD) technology, are capable of measuring distances to objects at high frame rates, however, the measured ranges and the intensity data contain systematic errors that need to be corrected. In this paper, a new integrated range camera self-calibration method via joint setup with a digital (RGB) camera is presented. This method can simultaneously estimate the systematic range error parameters as well as the interior and external orientation parameters of the camera. The calibration approach is based on photogrammetric bundle adjustment of observation equations originating from collinearity condition and a range errors model. Addition of a digital camera to the calibration process overcomes the limitations of small field of view and low pixel resolution of the range camera. The tests are performed on a dataset captured by a PMD[vision]-O3 camera from a multi-resolution test field of high contrast targets. An average improvement of 83% in RMS of range error and 72% in RMS of coordinate residual, over that achieved with basic calibration, was realized in an independent accuracy assessment. Our proposed calibration method also achieved 25% and 36% improvement on RMS of range error and coordinate residual, respectively, over that obtained by integrated calibration of the single PMD camera.

Multiobjective evolutionary algorithms: A survey of the state of the art

Abstract: A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

Abstract: Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

Stochastic Processes And Filtering Theory

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