Vision-Aided Navigation using Tracked Landmarks

<p>This thesis presents vision-based state estimation algorithms for autonomous vehicles to navigate within GPS-denied environments. To accomplish this objective, an approach is developed that utilizes a priori information about the environment. In particular, the algorithm leverages recognizable ‘landmarks’ in the environment, the positions of which are known in advance, to stabilize the state estimate. Measurements of the position of one or more landmarks in the image plane of a monocular camera are then filtered using an extended Kalman filter (EKF) with data from a traditional inertial measurement unit (IMU) consisting of accelerometers and rate gyros to produce the state estimate. Additionally, the EKF algorithm is adapted to accommodate a stereo camera configuration to measure the distance to a landmark using parallax. The performances of the state estimation algorithms for both the monocular and stereo camera configurations are tested and compared using simulation studies with a quadcopter UAV model. State estimation results are then presented using flight data from a quadcopter UAV instrumented with an IMU and a GoPro camera. It is shown that the proposed landmark navigation method is capable of preventing IMU drift errors by providing a GPS-like measurement when landmarks can be identified. Additionally, the landmark method pairs well with non a priori measurements for interims when landmarks are not available.</p>

Name thesis:degree_name

Master of Science in Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2018

Author dc:creator

• Myhre, Nicodemus

• navigation
• drones
• landmarks
• Aerospace Engineering
• Navigation, Guidance, Control and Dynamics

Repository record dc:identifier

https://commons.erau.edu/edt/390

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1389