Relocalization systems and methods

1. A method of determining a pose of an image capture device, comprising

capturing an image using an image capture device;

generating a data structure corresponding to the captured image;

comparing the data structure with a plurality of known data structures to identify a most similar known data structure;

reading metadata corresponding to the most similar known data structure to determine a pose of the image capture device; and

training a neural network by mapping a plurality of known images to the plurality of known data structures,

wherein the data structure is an N dimensional vector,

wherein generating the data structure corresponding to the captured image comprises using a neural network to map the captured image to the N dimensional vector,

wherein each known image of the plurality has respective metadata including pose data, and

wherein training the neural network comprises:

accessing a database of the known images annotated with the respective metadata;

decreasing a first Euclidean distance between first and second known N dimensional vectors respectively corresponding to matching first and second known images in an N dimensional space; and

increasing a second Euclidean distance between first and third known N dimensional vectors respectively corresponding to non-matching first and third known images in the N dimensional space.

2. The method of claim 1 , wherein the data structure is a compact representation of the captured image.

3. The method of claim 1 , wherein the neural network is a convolutional neural network.

4. The method of claim 1 , wherein the data structure is a 128 dimensional vector.

5. The method of claim 1 , wherein each of the plurality of known data structures is a respective known N dimensional vector in an N dimensional space.

6. The method of claim 5 , wherein comparing the data structure with the plurality of known data structures to identify the most similar known data structure comprises

determining respective Euclidean distances between the N dimensional vector and each respective known N dimensional vector, and

identifying a known N dimensional vector having a smallest distance to the N dimensional vector as the most similar known data structure.

7. The method of claim 1 , wherein each of the plurality of known data structures is a respective known 128 dimensional vector in a 128 dimensional space.

8. The method of claim 1 , wherein the neural network is a convolutional neural network.

9. The method of claim 1 , wherein training the neural network comprises modifying the neural network based on comparing a triplet of known images of the plurality.

10. The method of claim 9 , wherein each known data structure of the plurality is a respective known N dimensional vector in an N dimensional space,

wherein a first known image of the triplet is a matching image for a second known image of the triplet, and

wherein a third known image of the triplet is a non-matching image for the first known image of the triplet.

11. The method of claim 1 , wherein the pose data encodes a translation and a rotation of a camera corresponding to a known image.

12. The method of claim 1 , wherein a first Euclidean distance between respective first and second pose data corresponding to the matching first and second known images is less than a predefined threshold, and

wherein a second Euclidean distance between respective first and third pose data corresponding to the non-matching first and third known images is more than the predefined threshold.

13. The method of claim 1 , further comprising comparing the data structure with the plurality of known data structures to identify the most similar known data structure in real time.

14. The method of claim 1 , wherein the metadata corresponding to the most similar known data structure includes pose data corresponding to the most similar known data structure.

15. The method of claim 14 , further comprising determining a pose of the image capture device from the pose data in the metadata of the most similar known data structure.