This invention relates to the field of UAV
pose map optimization technology, and particularly to a method for optimizing the
pose map of a UAV
monocular camera. The method includes: blurring different
image frame data, constructing a
Gaussian pyramid, and downsampling; determining candidate feature points by subtracting
Gaussian blurred images of adjacent scales; accurately determining the positions of candidate feature points on the
image frame; removing low-contrast candidate feature points and edge response feature points to obtain the final feature points; and using the LM
algorithm to adjust the UAV attitude parameters and optimize the
pose map for discontinuous
image frame data. The process of
Gaussian difference point interest detection on continuously acquired image frame data suppresses
noise in the image frames, effectively
smoothing noise in the image. The resulting pose map is smoother with smaller gradient changes, improving the robustness of attitude
estimation. The pose map optimization process expands the scope of pose map optimization, allowing optimization even for discontinuous images.