Augmented reality navigation method based on indoor natural scene image deep learning
The invention discloses an augmented reality navigation method based on indoor natural scene image deep learning. The method comprises the steps of firstly scanning an indoor natural scene through a three-dimensional laser scanner to extract three-dimensional scene feature recognition points, then c...
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| creator | SUN JIAXIN TANG HAOCHEN BO YINGJIE LIU WEIWEI CAO XINGWEN LIAO ZONGYU WU MENGQUAN ZHANG CONGYING ZHANG WENLIANG TUO MINGYI ZHAO ZIQI NING XIANGYU ZHOU HUILIN |
| description | The invention discloses an augmented reality navigation method based on indoor natural scene image deep learning. The method comprises the steps of firstly scanning an indoor natural scene through a three-dimensional laser scanner to extract three-dimensional scene feature recognition points, then calculating an internal reference matrix of a smart phone camera, collecting an indoor natural sceneimage through a smart phone to extract two-dimensional image feature recognition points, and establishing an indoor natural scene topological network structure chart through an indoor planar map; binding and mapping the two-dimensional image feature recognition points, the three-dimensional scene feature recognition points and the topological network path nodes through specific descriptors; carrying out deep learning-based image classification on the indoor natural scene images acquired by the smart phone, and segmenting the indoor natural scene into a plurality of sub-scenes; and then tracking and recovering the thre |
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The method comprises the steps of firstly scanning an indoor natural scene through a three-dimensional laser scanner to extract three-dimensional scene feature recognition points, then calculating an internal reference matrix of a smart phone camera, collecting an indoor natural sceneimage through a smart phone to extract two-dimensional image feature recognition points, and establishing an indoor natural scene topological network structure chart through an indoor planar map; binding and mapping the two-dimensional image feature recognition points, the three-dimensional scene feature recognition points and the topological network path nodes through specific descriptors; carrying out deep learning-based image classification on the indoor natural scene images acquired by the smart phone, and segmenting the indoor natural scene into a plurality of sub-scenes; and then tracking and recovering the thre</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; GYROSCOPIC INSTRUMENTS ; HANDLING RECORD CARRIERS ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; MEASURING ; MEASURING ANGLES ; MEASURING AREAS ; MEASURING DISTANCES, LEVELS OR BEARINGS ; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS ; MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS ; NAVIGATION ; PHOTOGRAMMETRY OR VIDEOGRAMMETRY ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; SURVEYING ; TESTING</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200508&DB=EPODOC&CC=CN&NR=111126304A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200508&DB=EPODOC&CC=CN&NR=111126304A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SUN JIAXIN</creatorcontrib><creatorcontrib>TANG HAOCHEN</creatorcontrib><creatorcontrib>BO YINGJIE</creatorcontrib><creatorcontrib>LIU WEIWEI</creatorcontrib><creatorcontrib>CAO XINGWEN</creatorcontrib><creatorcontrib>LIAO ZONGYU</creatorcontrib><creatorcontrib>WU MENGQUAN</creatorcontrib><creatorcontrib>ZHANG CONGYING</creatorcontrib><creatorcontrib>ZHANG WENLIANG</creatorcontrib><creatorcontrib>TUO MINGYI</creatorcontrib><creatorcontrib>ZHAO ZIQI</creatorcontrib><creatorcontrib>NING XIANGYU</creatorcontrib><creatorcontrib>ZHOU HUILIN</creatorcontrib><title>Augmented reality navigation method based on indoor natural scene image deep learning</title><description>The invention discloses an augmented reality navigation method based on indoor natural scene image deep learning. The method comprises the steps of firstly scanning an indoor natural scene through a three-dimensional laser scanner to extract three-dimensional scene feature recognition points, then calculating an internal reference matrix of a smart phone camera, collecting an indoor natural sceneimage through a smart phone to extract two-dimensional image feature recognition points, and establishing an indoor natural scene topological network structure chart through an indoor planar map; binding and mapping the two-dimensional image feature recognition points, the three-dimensional scene feature recognition points and the topological network path nodes through specific descriptors; carrying out deep learning-based image classification on the indoor natural scene images acquired by the smart phone, and segmenting the indoor natural scene into a plurality of sub-scenes; and then tracking and recovering the thre</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>GYROSCOPIC INSTRUMENTS</subject><subject>HANDLING RECORD CARRIERS</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>MEASURING</subject><subject>MEASURING ANGLES</subject><subject>MEASURING AREAS</subject><subject>MEASURING DISTANCES, LEVELS OR BEARINGS</subject><subject>MEASURING IRREGULARITIES OF SURFACES OR CONTOURS</subject><subject>MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS</subject><subject>NAVIGATION</subject><subject>PHOTOGRAMMETRY OR VIDEOGRAMMETRY</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>SURVEYING</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNizEKAjEURLexEPUO3wMI6or9sihWVlovXzPGQPITkr-CtzeCB3CaYXhvps21G22AKAxlsHf6JuGXs6wuCgXoMxq6cam8bicmxlwNHTN7KncIyAW2IAMk8uAsTuy8mTzYFyx-PWuWx8OlP62Q4oCS-HvUoT9varb7dr3r2n-cD-xGOSQ</recordid><startdate>20200508</startdate><enddate>20200508</enddate><creator>SUN JIAXIN</creator><creator>TANG HAOCHEN</creator><creator>BO YINGJIE</creator><creator>LIU WEIWEI</creator><creator>CAO XINGWEN</creator><creator>LIAO ZONGYU</creator><creator>WU MENGQUAN</creator><creator>ZHANG CONGYING</creator><creator>ZHANG WENLIANG</creator><creator>TUO MINGYI</creator><creator>ZHAO ZIQI</creator><creator>NING XIANGYU</creator><creator>ZHOU HUILIN</creator><scope>EVB</scope></search><sort><creationdate>20200508</creationdate><title>Augmented reality navigation method based on indoor natural scene image deep learning</title><author>SUN JIAXIN ; 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The method comprises the steps of firstly scanning an indoor natural scene through a three-dimensional laser scanner to extract three-dimensional scene feature recognition points, then calculating an internal reference matrix of a smart phone camera, collecting an indoor natural sceneimage through a smart phone to extract two-dimensional image feature recognition points, and establishing an indoor natural scene topological network structure chart through an indoor planar map; binding and mapping the two-dimensional image feature recognition points, the three-dimensional scene feature recognition points and the topological network path nodes through specific descriptors; carrying out deep learning-based image classification on the indoor natural scene images acquired by the smart phone, and segmenting the indoor natural scene into a plurality of sub-scenes; and then tracking and recovering the thre</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING GYROSCOPIC INSTRUMENTS HANDLING RECORD CARRIERS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MEASURING MEASURING ANGLES MEASURING AREAS MEASURING DISTANCES, LEVELS OR BEARINGS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS NAVIGATION PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SURVEYING TESTING |
| title | Augmented reality navigation method based on indoor natural scene image deep learning |
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