Thanks for the equations. I compared L2 distance (rescaled between 0 and 1) versus dot product (rescaled between 0 and 1) and they are indeed proportional:

I updated the PR with the rescaled dot product version. The advantage of the the dot product over L2 is that it is much faster to compute (no sqrt). In both cases the highest hypothesis looks fine, however because the mean is so much larger than std, we get very large likelihood for the new place hypothesis computed by this equation:

Line 5291 in 11adbdc

likelihood.begin()->second = mean/stdDev + 1.0f;

From this paper:

For the new location probability St = −1, the likelihood is evaluated using (4) : p(Lt|St = −1) = L(St = −1|Lt) = μ/σ + 1 . If L(St = −1|Lt) is high (i.e., Lt is not similar to a particular location in WM, as σ < μ), then Lt is more likely to be a new location.

Here is the comparison of the raw likelihood (and effect on adjusted likelihood and bayes filter posterior) between netvlad dotproduct similarity, direct local features similarity (pairs/totalWords) and TF-IDF approaches on NewCollege dataset for a specific image.

NETVLAD dot product similarity:

Direct local features similarity (pairs/totalWords):

TF-IDF:

In conclusion, with global descriptors, we would need to make similarity less similar when images are not taken at the same place. This would decrease the mean and increase the std, so that adjusted likelihood for the new place would be around 2<->6 (like local descriptors approach) instead of 15<->20 (suppressing loop closure scores significantly). The current assumption for the likelhood is that std would be higher than mean if the place is similar only to a particular place. For netvlad similarity with mean>std, it means the current image looks a lot like any other images in the dataset, so probably best not loop on it as not enough discriminating. I guess at this point I would need to read more about how people used it for loop closure detection.

about GlobalDescriptor #1105

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