To navigate, robots need to locate themselves. In the case of unmanned aerial vehicles (UAVs), the standard solution consists in using GPS, INS and Compass measurements. Yet, this solution is not appropriate in difficult environment like indoors or close to large buildings; where GPS signals losses and erroneous orientation measurements from the compass are observed. Our aim here is to provide a reliable pose confidence domain; a box in which we are sure the robot is situated. In other words, we wish to compute a “safety area” around the robot that should be considered by the controller in order to avoid collisions with eventual robots or objects present in the navigation environment. GPS and compass unavailability can be overcome by using a camera in order to enhance robot localization. In computer vision, many solutions to pose estimation from a set of known landmarks (such as POSIT, PnP, etc. see [1] for a survey) exist but classically provide a punctual estimate of the robot location. Interval analysis is a powerful tool for rigorous uncertainty propagation (see [2] for a 3D vision application, and [3] for GPS position uncertainty domain computation). To quantify the robot pose uncertainty, we propose an interval-based set-membership approach, which computes over time a bounding box of the pose of the robot, taking image measurements and landmark positions uncertainties into account.