In this paper, we present a localization system for the use of drone in a remote lab. The objective is to allow a drone to inspect remote electronic instruments autonomously, as well as to return to its base and land on a platform for the recharge of its batteries. In addition, the drone should be able to detect the presence of a teacher in the lab, and to center the human face in the image in order to enable remote student-teacher communication. To achieve the first objective, the localization approach is composed of a monocular SLAM (Simultaneous Localization and Mapping) algorithm PTAM (Parallel Tracking and Mapping) and an estimation based on the homography transform. For the face-drone servoing, the approach is based on the 3D Candide model. Both approaches work in real-time. Quantitative and qualitative experiments are presented that show the robustness of both methods.