Some more illustrations for drone imagery processing:

def stable_groups(keypoints, groups, threshold):

    for kp in keypoints:

        matched = false

        for group in groups:

            mean_feature = get_mean_feature(group)

            recent_pixel = get_recent_pixel(group)

            if kd.feature - mean_feature < threshold and abs(lucas_kanade_optical_flow(recent_pixel)-kp.pixel) < threshold:

               group.add(kp)

               matched = true

               break

        if matched == false:

            groups.add(create_group(kp))

def global_groups(stable_groups,global_groups, threshold):

    for stable_group in stable_groups:

        matched = false

        for global_group in global_groups:

            mean_feature = get_mean_feature(global_group)

            recent_pixel = get_recent_pixel(global_group)

            if get_mean_feature(stable_group) - mean_feature < threshold and delta_least_squares(stable_group,global_group):

               global_group.add(stable_group)

               matched = true

               break

        if matched == false:

            global_groups.add(create_global_group(stable_group))

def spherical_gps_to_position_n_orientation(gps, frame):

    return (d,x,d,y,h)

def camera_angle(keypoint, resolutionW, resolutionH, field_of_view):

       return arctan((x1 x (tan(field_of_view)/2) ) / (W/2)

def world_coordinates(keypoint, drone_frame):

    # solve these equations

    # 1.      (di.h - si.h).tan-theta-i-x = s.x - di.x,

    # 2.      (di.h - si.h).tan-theta-i-y = s.y - di.y

    # return (s.x,s.y, s.h)