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  • 1.
    Jouybari, Arash
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.
    Amiri, Hadi
    School of Engineering Science, College of Engineering, University of Tehran, Iran.
    Ardalan, Alireza A.
    School of Surveying and Geomatics Engineering, University of Tehran, Iran.
    Zahraee, Niloofar K.
    School of Surveying and Geomatics Engineering, University of Tehran, Iran.
    Methods comparison for attitude determination of a lightweight buoy by raw data of IMU2019In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 135, p. 348-354Article in journal (Refereed)
    Abstract [en]

    Today, one of the most important issues is the determination of instantaneous sea level and distinguishing the Tsunami by floating buoy in the ocean. Usually, gyroscopes are used to measure the angular velocity of a buoy. On the other hand, considering the advancement of various technologies in the field of precise accelerometers, make it possible to use these kinds of sensors for navigation purpose. In this research, stable and optimal methods for determining the orientation of a moving buoy is presented using a combination of the gyroscope, accelerometers, and magnetic sensors data. In order to prove the effectiveness of the proposed methods, the raw data were collected from accelerometers, gyroscopes, and magnetometers of (Xsens MTI-G-700) mounted on a Buoy in coastal waters of Kish Island, Iran. Then, by using the proposed methods, the Euler angles of the buoy are determined, while the Euler angles are derived from the Xsens sensor we are considered as a reference. Based on the results, RMSD for Madgwick algorithm are 0.57° 0.37° and 0.50° for Mahony algorithm are 0.56° 0.37° and 0.50° and finally for Complementary algorithm is 0.63° 0.26° and 2.38° which these values are for roll, pitch, and yaw angles respectively. Thus Mahony algorithm for determining roll and yaw Euler angles is more accurate than other algorithms; however, this differences is negligible compared to the Madgwick algorithm. The Complementary algorithm is less accurate than the other two algorithms, especially for determining the yaw angle of the buoy.

  • 2.
    Khodaverdi, Niloofar
    et al.
    School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
    Rastiveis, Heidar
    School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
    Jouybari, Arash
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.
    Combination of Post-Earthquake LiDAR Data and Satellite Imagery for Buildings Damage Detection2019In: Earth Observation and Geomatics Engineering, ISSN 2588-4352, Vol. 3, no 1, p. 12-20Article in journal (Refereed)
    Abstract [en]

    Earthquakes are known as one of the deadliest natural disasters that have caused many fatalities and homelessness through history. Due to the unpredictability of earthquakes, quick provision of buildings damage maps for reducing the number of losses after an earthquake has become an essential topic in Photogrammetry and Remote Sensing. Low-accuracy building damage maps waste the time that is required to rescue the people in destructed areas by wrongly deploying the rescue teams toward undamaged areas. In this research, an object-based algorithm based on combining LiDAR raster data and high-resolution satellite imagery (HRSI) was developed for buildings damage detection to improve the relief operation. This algorithm combines classification results of both LiDAR raster data and high-resolution satellite imagery (HRSI) for categorizing the area into three classes of “Undamaged,” “Probably Damaged,” and “Surely Damaged” based on the object-level analysis. The proposed method was tested using Worldview II satellite image and LiDAR data of the Port-au-Prince, Haiti, acquired after the 2010 earthquake. The reported overall accuracy of 92% demonstrated the high ability of the proposed method for post-earthquake damaged building detection.

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