OpenCV Feature matching to check similarity between two scenes

Question

I am working on a project and I have to make an AR drone follow a path based on a list of checkpoints which are saved in a directory. Each checkpoint is a scene that the drone should detect along its path. There could be differences between the checkpoints and the actual scenes in terms of brightness, small obstacles present in the actual scenes or small variation of the point of view. To detect the checkpoints while the drone was moving, I have decided to use feature matching to get the number of good matches and the ratio between the inliers and the number of good matches and use these parameters to check if the checkpoint has been reached or not.

Algorithm:

1. convert the image to grayscale
2. Use a detector to detect the keypoints (I have tried SIFT,SURF, ORB and AKAZE)
3. Use an extractor to calculate the feature vectors
4. Use a matching algorithm to perform the matching (I have tried Bruteforce and Bruteforce-Hamming)
5. Keep only the good matches and compute the number of inliers.
6. Check if the ratio between inliers and good matches is above a threshold and the number of good_matches is above another threshold. If this condition holds, then, the checkpoint has been matched.

Results: The checking algorithm is roughly good but sometimes it detects a checkpoint, that was taken from a landed drone, only after crossing it. While, with the same checkpoint, the checking algorithm does not detect it if the drone is slightly shifted to the left compared to the position in which the checkpoint has been taken.

Is it a good approach for this problem or is there a better way to reach my goal? If it is a good way, how can I improve the checking when the drone is close to the checkpoint?

The code that implements the feature matching is shown below:

matcher->knnMatch(desc1, desc2, dmatches, KNN_best_matches);
vector<Point2f> matches, inliers;
if(matches2points_nndr(kp1,kp2,dmatches,matches,DRATIO,MIN_MATCH_COUNT)){
    *match = true;

    //compute inliers
    compute_inliers_ransac(matches, inliers, MAX_H_ERROR, false);

    //update stats
    stats.matches = (int)matches.size()/2;
    stats.inliers = (int)inliers.size()/2;
    stats.outliers = stats.matches - stats.inliers;
    stats.ratio = (float) stats.inliers * 100.0 / (float) stats.matches;
}

In another class, stats.ratio is compared with a threshold.

if(draw_stats.ratio > threshold_matching){
    //move to the next checkpoint
    match = true;
}else{
    std::cout << "ratio is under the threshold: " << draw_stats.ratio << std::endl;
    match = false;
}