Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Position System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates the incorporation of a monocular vision sensor into a standard avionics suite to assist with autonomous operation of aerial vehicles in GPS-denied environments. An Extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is proposed. When GPS is available, multiple observations of a single landmark point from the vision sensor allow the point's location in inertial space to be estimated. When GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS scenario are presented to verify the proposed method.