Semantics based safe landing area detection for an unmanned vehicle
First Claim
1. A method for determining a suitable landing area for an aircraft, comprising:
- receiving signals indicative of terrain information for a terrain via a three-dimension (3D) perception system;
receiving signals indicative of image information for the terrain via a camera perception system, the image information separate from the terrain information;
evaluating, with the processor, the terrain information and generating information indicative of a landing zone candidate region;
co-registering in a coordinate system, with the processor, the landing zone candidate region and the image information;
segmenting, with the processor, an image region from the image information corresponding to the landing zone candidate region to generate segmented regions;
classifying, with the processor, the segmented regions into semantic classes;
determining, with the processor, contextual information from a contextual model and using the contextual information to detect an error in at least one semantic classes; and
ranking and prioritizing the semantic classes.
1 Assignment
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Accused Products
Abstract
A method for determining a suitable landing area for an aircraft includes receiving signals indicative of Light Detection And Ranging (LIDAR) information for a terrain via a LIDAR perception system; receiving signals indicative of image information for the terrain via a camera perception system; evaluating, with the processor, the LIDAR information and generating information indicative of a LIDAR landing zone candidate region; co-registering in a coordinate system, with the processor, the LIDAR landing zone candidate region and the image information; segmenting, with the processor, the co-registered image and the LIDAR landing zone candidate region to generate segmented regions; classifying, with the processor, the segmented regions into semantic classes; determining, with the processor, contextual information in the semantic classes; and ranking and prioritizing the contextual information.
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Citations
23 Claims
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1. A method for determining a suitable landing area for an aircraft, comprising:
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receiving signals indicative of terrain information for a terrain via a three-dimension (3D) perception system; receiving signals indicative of image information for the terrain via a camera perception system, the image information separate from the terrain information; evaluating, with the processor, the terrain information and generating information indicative of a landing zone candidate region; co-registering in a coordinate system, with the processor, the landing zone candidate region and the image information; segmenting, with the processor, an image region from the image information corresponding to the landing zone candidate region to generate segmented regions; classifying, with the processor, the segmented regions into semantic classes; determining, with the processor, contextual information from a contextual model and using the contextual information to detect an error in at least one semantic classes; and ranking and prioritizing the semantic classes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A system for determining a suitable landing area for an aircraft, comprising:
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a processor; and memory having instructions stored thereon that, when executed by the processor, cause the system to; receive signals indicative of terrain information for a terrain with a three-dimension (3D) perception system; receive signals indicative of image information for the terrain with a camera perception system, the image information separate from the terrain information; evaluate the terrain information and generate information indicative of a landing zone candidate region; co-register in a coordinate system the landing zone candidate region and the image information; segment image regions corresponding to the landing zone candidate regions to generate segmented regions; classify the segmented regions into semantic classes; determine contextual information from a contextual model and using the contextual information to detect an error in at least one semantic class; and ranking and prioritizing the semantic class. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
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Specification