Detection of invariant features for localization
First Claim
1. A method comprising:
- providing to a trained neural network a first image, wherein the first image (a) is captured by an image capture device at a first location and at a first pose, (b) is associated with geophysical coordinates corresponding to the first location, and (c) comprises one or more static features;
providing to the trained neural network a second image, wherein the second image (a) is captured by an image capture device at a second location and at a second pose, (b) is associated with geophysical coordinates corresponding to the second location, and (c) comprises at least one of the one or more static features;
identifying the at least one of the one or more static features in both the first and second images with a branch of the trained neural network;
generating a three dimensional image comprising the at least one identified static feature and based on the first and second images;
extracting a three dimensional geometry of the at least one identified static feature;
encoding the extracted three dimensional geometry as an array;
based on the first location and second location, determining a static feature location for the at least one identified static feature; and
storing the array in association with a map tile, wherein the map tile is selected based on the static feature location.
1 Assignment
0 Petitions
Accused Products
Abstract
A first image and a second image are provided to a trained neural network. The first image comprises one or more static features and the second image comprises at least one of the one or more static features. A static feature is identified in both the first and second images by a branch of the trained neural network. A three dimensional image comprising the identified static feature is generated and three dimensional geometric information/data related to the static feature is extracted and stored in association with a tile of a digital map. A set of training images may be used to train the trained neural network comprises training image subsets comprising two or more images that substantially overlap that were (a) captured at different times; (b) captured under different (i) weather conditions, (ii) lighting conditions, or (iii) weather and lighting conditions; or both a and b.
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Citations
20 Claims
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1. A method comprising:
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providing to a trained neural network a first image, wherein the first image (a) is captured by an image capture device at a first location and at a first pose, (b) is associated with geophysical coordinates corresponding to the first location, and (c) comprises one or more static features; providing to the trained neural network a second image, wherein the second image (a) is captured by an image capture device at a second location and at a second pose, (b) is associated with geophysical coordinates corresponding to the second location, and (c) comprises at least one of the one or more static features; identifying the at least one of the one or more static features in both the first and second images with a branch of the trained neural network; generating a three dimensional image comprising the at least one identified static feature and based on the first and second images; extracting a three dimensional geometry of the at least one identified static feature; encoding the extracted three dimensional geometry as an array; based on the first location and second location, determining a static feature location for the at least one identified static feature; and storing the array in association with a map tile, wherein the map tile is selected based on the static feature location. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An apparatus comprising at least one processor and at least one memory storing computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
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provide to a trained neural network a first image, wherein the first image (a) is captured by an image capture device at a first location and at a first pose, (b) is associated with geophysical coordinates corresponding to the first location, and (c) comprises one or more static features; provide to the trained neural network a second image, wherein the second image (a) is captured by an image capture device at a second location and at a second pose, (b) is associated with geophysical coordinates corresponding to the second location, and (c) comprises at least one of the one or more static features; identify the at least one of the one or more static features in both the first and second images with a branch of the trained neural network; generate a three dimensional image comprising the at least one identified static feature and based on the first and second images; extract a three dimensional geometry of the at least one identified static feature; encode the extracted three dimensional geometry as an array; based on the first location and second location, determine a static feature location for the at least one identified static feature; and store the array in association with a map tile, wherein the map tile is selected based on the static feature location. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions configured to:
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provide to a trained neural network a first image, wherein the first image (a) is captured by an image capture device at a first location and at a first pose, (b) is associated with geophysical coordinates corresponding to the first location, and (c) comprises one or more static features; provide to the trained neural network a second image, wherein the second image (a) is captured by an image capture device at a second location and at a second pose, (b) is associated with geophysical coordinates corresponding to the second location, and (c) comprises at least one of the one or more static features; identify the at least one of the one or more static features in both the first and second images with a branch of the trained neural network; generate a three dimensional image comprising the at least one identified static feature and based on the first and second images; extract a three dimensional geometry of the at least one identified static feature; encode the extracted three dimensional geometry as an array; based on the first location and second location, determine a static feature location for the at least one identified static feature; and store the array in association with a map tile, wherein the map tile is selected based on the static feature location.
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Specification