Towards Autonomous Fixed-Wing Unmanned Aerial Vehicle Landing: A Vision-Aided Inertial Navigation under Sensor Reconfiguration Scenario

While autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation, the standard inertial navigation unit (INU, the inertial measurement unit with a global positioning system (GPS)) provides relatively poor accuracy in altitude estimation. A common solution for this problem is to aid the INU with additional sensors and/or ground infrastructures, but the main hurdles to the approach are the limited payload of UAVs and extra cost involved. Dynamic sensor reconfiguration can be a good alternative by constructing a new sensor system utilizing available sensors around without adding new sensory equipment to UAVs. In this paper, a sensor reconfiguration scenario for autonomous fixed-wing UAV landing is considered and the resulting vision-aided inertial navigation system is investigated. This paper presents (i) a sensor fusion algorithm for a passive monocular camera and an INU based on the Extended Kalman Filter (EKF), and (ii) an object-detection vision algorithm using optical flow. The EKF is chosen to take care of the nonlinearities in the vision system, and the optical flow is used to robustly detect the UAV from noisy background. Pilot-controlled landing experiments on a NASA UAV platform and the filter simulations were performed to validate the feasibility of the proposed approach. Promising results were obtained showing 50%-80% error reduction in altitude estimation.