Not quite the same application, but in computer vision and visual SLAM algorithms (to construct a map of your surrounding using a camera) embeddings have become

Not quite the same application, but in computer vision and visual SLAM algorithms (to construct a map of your surrounding using a camera) embeddings have become a de-facto method to perform place-recognition ! And it's very similar to this article. It is called "bag-of-word place recognition" and it really became the standard, used by absolutely every open-source library nowadays. The core idea is that each image is passed through a feature-extractor-descriptor pipeline and is 'embedded' in a vector containing the N top features. While the camera moves, a database of images (called keyframes) is created (images are stored as much-lower dimensional vectors). Again while the camera moves, all images are used to query the database, something like cosine-similarity is used to retrieve the best match from the vector database. If a match happened, a stereo-constraints can be computed betweeen the query image and the match, and the software is able to update the map. [1] is the original paper and here's the most famous implementation: https://github.com/dorian3d/DBoW2 https://github.com/dorian3d/DBoW2 [1]: https://www.google.com/search?client=firefox-b-d&q=Bags+of+Binary+Words+for+Fast+Place+Recognition+in+Image+Sequences https://www.google.com/search?client=firefox-b-d&q=Bags+of+B...