Vision-aided aerial navigation
First Claim
1. A method of navigation of an aerial vehicle, the method comprising:
- acquiring an aerial image frame from a camera on the aerial vehicle;
partitioning the acquired aerial image frame into a plurality of regions;
classifying one or more of the plurality of regions as featureless or feature-rich by computing an autocorrelation function to determine at least one peak width, and testing the at least one peak width against a threshold;
extracting features for those regions classified as feature-rich and not for those regions classified as featureless; and
navigating the aerial vehicle based at least in part on the extracted features.
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Accused Products
Abstract
An aerial vehicle is navigated using vision-aided navigation that classifies regions of acquired still image frames as featureless or feature-rich, and thereby avoids expending time and computational resources attempting to extract and match false features from the featureless regions. The classification may be performed by computing a texture metric as by testing widths of peaks of the autocorrelation function of a region against a threshold, which may be an adaptive threshold, or by using a model that has been trained using a machine learning method applied to a training dataset comprising training images of featureless regions and feature-rich regions. Such machine learning method can use a support vector machine. The resultant matched feature observations can be data-fused with other sensor data to correct a navigation solution based on GPS and/or IMU data.
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Citations
20 Claims
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1. A method of navigation of an aerial vehicle, the method comprising:
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acquiring an aerial image frame from a camera on the aerial vehicle; partitioning the acquired aerial image frame into a plurality of regions; classifying one or more of the plurality of regions as featureless or feature-rich by computing an autocorrelation function to determine at least one peak width, and testing the at least one peak width against a threshold; extracting features for those regions classified as feature-rich and not for those regions classified as featureless; and navigating the aerial vehicle based at least in part on the extracted features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for navigation of an aerial vehicle comprising:
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a camera configured to acquire an aerial image frame; an image processor comprising; a partitioner configured to partition an acquired aerial image frame into a plurality of regions; a classifier configured to classify one or more of the plurality of regions as featureless or feature-rich by computing an autocorrelation function to determine at least one peak width, and testing the at least one peak width against a threshold; and a feature extractor configured to extract features only for those regions classified as feature-rich and not for those regions classified as featureless; and navigation controls configured to navigate the aerial vehicle based at least in part on the extracted features. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A method of navigation of an aerial vehicle comprising:
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acquiring an aerial image frame from a camera on the aerial vehicle; partitioning the acquired aerial image frame into regions; classifying one or more regions as only one of featureless or feature-rich, before extracting features from said regions, the classifying using a model that has been trained using a machine learning method applied to a training dataset comprising a plurality of training images of featureless regions and a plurality of training images of feature-rich regions; extracting, from the aerial image frame, features for those regions classified as feature-rich and not for those regions classified as featureless; and navigating the aerial vehicle based at least in part on the extracted features. - View Dependent Claims (17, 18, 19, 20)
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Specification