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Final Project: Fine-grained localisation
Project type: Group (teams of 2)
Submission: Source code and written report (as .pdf)
Marks: The assignment will be marked out of 30 points, and will contribute 30% of your
total mark.
Geolocation is the problem of localising a person or device in the world using sensor data. Depending
on the device, the environment, and the level of accuracy required, geolocation may rely on GPS
coordinates, network routing addresses, or image data. Geolocation is an important problem in many
AI and computing applications, from autonomous vehicle navigation to search engine queries based
on the user’s current location (e.g., “restaurants near me”).
In this project, you will investigate the problem of fine-grained geolocation in a small indoor/outdoor
environment (an art museum). Image information is particularly important for this type of problem,
because other sources of information, like GPS, may not be accurate enough to provide fine-grained
position data and may not be able to distinguish between different floors in indoor environments.
Your task is to develop a method to recognize the location from which an image was taken. You
will be provided a dataset of images with position data to train your method. How you approach the
problem is up to you. The following are some possible approaches:
? Match each image in the test set to the most similar image in the training set, using any visual
features you wish to measure “similarity,” and assume the test image has the same position data
as its closest match. (Note that there is no guarantee that the test images will come from exactly
the same locations as the images in training set, but since they come from the same museum
environment they are likely to be from nearby locations.)
Identify key features, objects, or text in the test images and use these to locate training images
which show the same features, objects, or text.
Match each image in the test set to multiple near neighbours in the training set, and develop a
method to compute the test image’s most likely location based on multiple nearby views.
Use matching features and geometric constraints to compute the likely change in pose between
training and test views.