This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

<p>The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased over the last years. This has been possible mainly due to the increased computing power of microcontrollers and computers. An UAV can be used in both civilian and military areas, for example surveillance and intelligence. The UAV concerned in this master's thesis is a prototype and is currently being developed at DST Control AB in Linköping.</p><p>With the use of UAVs, the need for a positioning and navigation system arises. Inertial sensors can often give a good position estimation, however, they need continuous calibration due to error build-up and drift in gyros. An external reference is needed to correct for this drift and other errors. The positioning system investigated in this master's thesis is supposed to work in an area defined by an inverted cone with the height of 25m and a diameter of 10m.</p><p>A comparison of different techniques suitable for position sensing has been performed. These techniques include the following: a radio method based on the Instrument Landing System (ILS), an optical method using a Position Sensing Detector (PSD), an optical method using the Indoor GPS system, a distance measurement method with ultrasound and also a discussion of the Global Positioning System (GPS).</p><p>An evaluation system has been built using the PSD sensor and tests have been performed to evaluate its possibilities for positioning. Accuracy in the order of a few millimetres has been achieved in position estimation with the evaluation system.</p>

The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased over the last years. This has been possible mainly due to the increased computing power of microcontrollers and computers. An UAV can be used in both civilian and military areas, for example surveillance and intelligence. The UAV concerned in this master's thesis is a prototype and is currently being developed at DST Control AB in Linköping. With the use of UAVs, the need for a positioning and navigation system arises. Inertial sensors can often give a good position estimation, however, they need continuous calibration due to error build-up and drift in gyros. An external reference is needed to correct for this drift and other errors. The positioning system investigated in this master's thesis is supposed to work in an area defined by an inverted cone with the height of 25m and a diameter of 10m. A comparison of different techniques suitable for position sensing has been performed. These techniques include the following: a radio method based on the Instrument Landing System (ILS), an optical method using a Position Sensing Detector (PSD), an optical method using the Indoor GPS system, a distance measurement method with ultrasound and also a discussion of the Global Positioning System (GPS). An evaluation system has been built using the PSD sensor and tests have been performed to evaluate its possibilities for positioning. Accuracy in the order of a few millimetres has been achieved in position estimation with the evaluation system.