Course project for CSE 544
(grad-level Database Systems).
In collaboration with Joshua Fan
As a robot navigates in an indoor environment, it can collect a dataset of local observations using the on-board laser range-finder, which can then be used to train machine learning models for place classification.
The goal of our project is to enable storing images of laser-range observations into a relational database, and retrieving similar images to a query image efficiently. We build a pipeline to extract image features and store them into a relational database following the Bag-of-Visual-Words model, and we implemented FIDS ("Flexible Image Database System") to speed up image retrieval, as well as Locality Sensitive Hashing to store data in a way that allows for faster retrieval. See abstract and report below.
