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Heading angle Observability Enhancement in Visual Inertial Navigation via addition of Magnetometer for GPS Denied Environment

Pahlevani, Ali | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56574 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Pourtakdoust, Hossein
  7. Abstract:
  8. In this thesis, a method is presented for improving the observability of the heading angle in a UAV when using the Visual-Inertial navigation algorithm MSCKF by adding a magnetometer sensor. The proposed algorithm serves as a complementary extension to the primary algorithm, and efforts have been made to seamlessly integrate the magnetometer sensor into the primary algorithm to allow for easy modifications to the sensor's characteristics and to observe the resulting output in system simulations. Improving the observability of the heading angle will lead to an enhancement in the overall system state estimation. To achieve this, a new update stage is added to the existing algorithm. Furthermore, the magnetometer bias is added to the state vector of the filter and by estimating it and reducing the estimated bias from the magnetometer measurements at each time step, the estimation accuracy increases. The main limitation of this method is that, in the absence of a suitable estimate of the system's attitude (referring to the Euler angles obtained in the previous time step), providing an accurate estimate of the aircraft's heading angle using magnetometer data may not be possible. As a result, it is possible that the estimation of other states may also be compromised. To further elaborate, for improving the accuracy of the heading angle estimate, it is necessary to employ a high-quality IMU and an appropriate filter for estimating the system's attitude. Additionally, the accuracy of the heading angle estimate is also dependent on the precision of the magnetometer measurements. Finally, it should be noted that the main innovations in this thesis are twofold. The first involves the calculation (estimation) of magnetometer measurements, which can be added to the primary EuRoc dataset, enabling the use of various algorithms for state estimation with the EuRoc dataset when both IMU and cameras, along with magnetometer data, are available. The primary innovation also involves improving the MSCKF algorithm, enabling the use of the generated magnetometer data in state estimation, particularly for the UAV's position and heading angle
  9. Keywords:
  10. Visual-Inertial Navigation ; Heading Angle ; Mult-State Constraint Kalman Filter (MSCKF)Algorithm ; Unmanned Aerial Vehicles (UAV) ; Visual Odometry ; Global Position System (GPS) ; Unmanned Aerial Vehicles (UAV)Visual-Inertial Navigation ; Visual-Inertial Odometry ; Adding Magnetometer to MSCKF Algorithm

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