Research on monocular visual FastSLAM for a small unmanned helicopter

For the autonomous flight of a small unmanned helicopter in a GPS-denied environment, a fast simultaneous localization and mapping (FastSLAM) algorithm based on Rao-Blackwellized particle filter (RBPF) was designed, and a monocular visual SLAM system for small unmanned helicopters in GPS-denied environments was implemented by using the Fast SLAM algorithm. The onboard monocular camera of the system uses the scale invariant feature transform (SIFT) to detect and match landmarks. The visual observation is fused with the inertial measurement to estimate the state of the vehicle and build the feature map simultaneously. An undelayed inverse depth parametrization method is applied to the landmark initialization. The stability and the effectiveness of this system were verified by simulations. The real flight experiments were also carried out to test the performance of the algorithm. The results show that the proposed system can estimate the state of the vehicle with higher accuracies in all items such as attitude, velocity and position, compared with the traditional GPS/INS navigation system. It can provide reliable navigation information for small unmanned helicopters in GPS-denied environments.