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Dynamics-Enabled Localization of UAVs using Unscented Kalman Filter

Omotuyi, Oyindamola
http://orcid.org/0000-0003-2655-1372
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065656007951

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
This thesis aims to develop a modular sensor fusion framework based on Unscented Kalman Filter (UKF) that estimates the 6-DOF (Degree-of-Freedom) pose of a quadrotor UAV using the dynamics derived from Newton's laws of motion and localization systems such as visual-inertial odometry (VIO) system and GPU-IMU (GI) system for both indoor and outdoor environments. Micro Aerial Vehicles (MAVs), especially quadrotors, are gaining attention for applications such as package delivery, inspection, emergency response, and search and rescue missions. State estimation becomes very crucial for carrying out both remotely-controlled and autonomous operations. This problem, known as localization, has been explored in the literature using a wider range of sensors such as radars, lidars, cameras, IMUs, and global positioning systems (GPS). In outdoor environments, GPS provides a reliable source of information for carrying out localization. Onboard sensing means such as cameras, IMUs, radars, or lidars are used for indoor environments. The localization problem becomes challenging for indoor environments for several reasons: i) difficulty in processing information from these sensors; ii) most onboard sensors are prone to erroneous measurements; and iii) need specific environmental conditions to satisfy (such as the presence of unique features in the environment, adequate lighting). This thesis focuses on improving localization by incorporating the UAV dynamics into the estimation alongside various localization sensors. We used a monocular camera and an IMU as sensing devices for indoor localization while GPS and IMU for outdoor localization. In recent times, VIO has been explored using different approaches. However, few research works exploit the quadrotor Newtonian dynamics and the known thrust and torque inputs. Incorporating the information from the dynamics with known control inputs provide robust state estimation. Hence, this thesis aims to estimate the quadrotor UAV 6-DOF pose by fusing the quadrotor dynamics coupled with VIO using UKF called VI-D. The visual-inertial-dynamics-based odometry incorporates the inputs from the state-of-the-art VIO system, VINS-Mono. For outdoor applications, the rate at which the GPS sensors publish their data affects the state estimation. This work studied this effect by variating the frequency from 0.5-5Hz and improving the estimation by adding information from the UAVs dynamics using UKF called GI-D. The GI data is obtained from PX4 Autopilot Estimation and Control Library (ECL). The UKF framework is divided into a prediction and an update step. The prediction step utilizes the quadrotor dynamics with inputs from the thrust force and torque. The VIO and GI-system provide 6-DOF pose, velocity, and unbiased angular velocity measurements used in the filter's update step. The proposed framework provides results with high update rates. In addition, the filter is independent of the VIO or the GPS-IMU system. Therefore, the approach is modular, and the filter can be used with any of these systems. We evaluated the effectiveness of the proposed methods through extensive simulations. VI-D results show that our approach improves the state estimates by 6-8% in translation and 19-49% in rotation for the VIO-based estimation. Similarly, GI-D improved GI-based navigation translation estimate by 3-8% and rotational estimate by 40-42%.
Manish Kumar, Ph.D. (Committee Chair)
David Thompson (Committee Member)
Rajnikant Sharma, Ph.D. (Committee Member)
66 p.
Robots
Visual Inertial Odometry; State Estimation; UAV Localization; Visual Inertial Dynamics Odometry; Indoor Localization using UKF; GPS localization using quadrotor dynamics

Recommended Citations

Citations

  • Omotuyi, O. (2021). Dynamics-Enabled Localization of UAVs using Unscented Kalman Filter [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065656007951

    APA Style (7th edition)

  • Omotuyi, Oyindamola. Dynamics-Enabled Localization of UAVs using Unscented Kalman Filter. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065656007951.

    MLA Style (8th edition)

  • Omotuyi, Oyindamola. "Dynamics-Enabled Localization of UAVs using Unscented Kalman Filter." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065656007951

    Chicago Manual of Style (17th edition)

ucin1637065656007951
318
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This open access ETD is published by University of Cincinnati and OhioLINK.