Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position

Vision-Aided Navigation using Tracked Lankmarks

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

Theoretical Limits of Lunar Vision Aided Navigation with Inertial Navigation System

The precision navigation capabilities of the Global Positioning System (GPS) are used extensively within US military operations. However, GPS is highly vulnerable to intentional and unintentional external interference. Therefore, a need exists to develop a non-GPS precision navigation method to operate in GPS degraded environments. This research effort presents the theoretical limits of a precision navigation method based on an inertial navigation system (INS) aided by angle measurements with respect to lunar surface features observed by a fixed camera. To accomplish this task, an extended Kalman filter (EKF) was implemented to estimate INS drift errors and bring in simulated lunar feature angle measurements to correct error estimates. The research scope focused solely on the feasibility of lunar vision aided navigation with INS where only measurement noise effects from a simulated CCD camera and barometer were considered. Various scenarios based on camera specifications, lunar feature quantity, INS grade, and lunar orbital parameters were conducted to observe the INS drift correction by lunar feature angle measurements. The resulting trade spaces presented by the scenarios showed theoretical substantial improvement in the navigation solution with respect to a stand alone INS