Synthetic Vision Method For UAV Navigation

Unmanned Aerial Vehicle (UAV) is one of automatic intelligence robots, due to the outstanding performance on aerial photography, disaster investigation, emergency and natural resource exploration, as well as the special role of the military, it attracts more attentions in military and air power. As one of the UAV key technologies, visual navigation was studied a lot in recent years, which could rebuild UAV flight environment for secure and autonomous flight in complex environments.Using vision sensor and aiming the development direction of UAV visual navigation, while stereo vision and structure from motion (SFM) are investigated, we focus on the research with the basic principle of high precision monocular synthetic vision, exploiting the robust algorithm and establishing the complete software system.On base of motion parallax decomposition, a synthetic vision three-dimensional (3D) reconstruction algorithm based on SURF (Speed up Robust Features) is proposed. First, remove the part of planar plane parallax by SURF characterization motion compensation. Then, the image Laplace pyramid is created to reduce the lighting variation, and the structure parameters are estimated on each pyramid level with residual parallax algorithm and refinement. At last, the 3D reconstruction is completed with the parameters given camera matrix and location information. With the quantitative analysis of the simulation scene and the qualitative analysis of the outdoor scene, the approach with higher precision and better robustness are obtained in comparison with stereo vision and SFM.