Vision-Aided Inertial Navigation System Design for Indoor Quadrotors

Abstract

The navigation task for unmanned aerial vehicles (UAVs), such as quadrotors, in an indoor environment becomes challenging as the global positioning system (GPS) and the magnetometer may provide inaccurate aiding measurements and the signals may get jammed. The navigation system design in this thesis integrates a visual navigation block with a inertial navigation system block, which adds information about aiding measurements information for indoor navigation design. The direct visual measurements are feature coordinates that are obtained from images taken from an onboard monocular camera with different positions in the 3D world space. The scaled relative pose measurements are generated through vision algorithm implementations presented in this thesis. The vehicle states are estimated using the extended Kalman filter (EKF) with inputs from a gyroscope and accelerometer. The EKF sensor fusion process combines inertial measurements and the visual aid- ing measurement to get an optimal estimation. This thesis provides two design results: one navigation system assumes that the 3D world feature coordinates are known and that the navigation system is map-based for the feature ex- traction. The other navigation system does not require prior knowledge of the feature location and captures the feature based on map-less vision algorithms with geometry constraints.