IP Library Granted Patent US 12,283,012
Granted Patent B2
US 12,283,012 · App. 18/510,623 · Granted Apr 22, 2025

Cross reality system with prioritization of geolocation information for localization

Inventors: Xuan Zhao (San Jose, CA); Christian Ivan Robert Moore (Cupertino, CA); Sen Lin (San Jose, CA); Ali Shahrokni (San Jose, CA); Ashwin Swaminathan (Dublin, CA)
Assignee: Magic Leap, Inc.
G06T19/006G06F9/547G06F16/29G06F16/907
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,283,012
App. No.
18/510,623
Granted
Apr 22, 2025
Kind
B2
Abstract

A cross reality system enables any of multiple devices to efficiently access previously stored maps. Both stored maps and tracking maps used by portable devices may have any of multiple types of location metadata associated with them. The location metadata may be used to select a set of candidate maps for operations, such as localization or map merge, that involve finding a match between a location defined by location information from a portable device and any of a number of previously stored maps. The types of location metadata may prioritized for use in selecting the subset. To aid in selection of candidate maps, a universe of stored maps may be indexed based on geo-location information. A cross reality platform may update that index as it interacts with devices that supply geo-location information in connection with location information and may propagate that geo-location information to devices that do not supply it.

Claims (72)

1. A cloud service for a cross reality system, the service comprising:

computer storage media storing:

a database of maps;

a plurality of types of location metadata associated with maps of the database, the plurality of types of location metadata comprising wireless fingerprints and geo-location information;

one or more computing devices configured for network communication with a plurality of portable electronic devices, the one or more computing devices comprising non-transitory computer-readable media comprising computer-executable instructions that, when executed perform a method comprising:

receiving, from a portable electronic device of the plurality of portable electronic devices, a communication comprising position information for the device and location metadata;

selecting, for the communication, a type of location metadata of the plurality of types of location metadata;

selecting a set of maps from the database based on the received location metadata and the selected type of location metadata associated with maps of the database; and

comparing the received position information to maps of the set.

2. The cloud service of claim 1 , wherein the received position information comprises a device tacking map.

3. The cloud service of claim 2 , wherein:

comparing comprises identifying a matching map from the set; and

the method further comprises merging the tracking map with the matching map.

4. The cloud service of claim 3 , wherein merging the tracking map with the matching map comprises:

associating location metadata from the tracking map with the merged map in the database of maps; and

storing geo-location information derived from the tracking map as an index of a record of a geo-location database, wherein the record identifies at least a portion of the merged map.

5. The cloud service of claim 1 , wherein:

the comparing comprises attempting to localize the device to a map of the set; and

the method further comprises, based on successfully localizing the device to a location in a stored map, transmitting to the device geo-location information derived from location metadata stored in association with the location in the stored map.

6. The cloud service of claim 5 , wherein:

the method further comprises, based on successfully localizing the device to a location in a stored map, storing geo-location information derived from the received location metadata as location metadata associated with the location in the stored map.

7. The cloud service of claim 6 , wherein:

storing geo-location information as location metadata associated with the location in the stored map comprises:

hashing the geo-location information; and

updating a database indexed by geo-hash value with the hashed geo-location information.

8. The cloud service of claim 5 , wherein:

the method further comprises, based on unsuccessfully localizing the device, storing geo-location information derived from the received location metadata as location metadata associated with a location in a stored map against which the device previously successfully localized.

9. A system comprising a plurality of portable electronic devices operating in a physical environment and configured to operate within a cross reality system to provide a shared experience of virtual content to users of the plurality of portable electronic devices, wherein:

each of the portable electronic device comprises:

one or more sensors configured to capture information about an environment, the captured information comprising a plurality of images; and

at least one processor configured to execute computer-executable instructions, wherein the computer-executable instructions comprise instructions for:

extracting a plurality of features from images captured with the one or more sensors;

generating location metadata indicative of a physical location of the portable electronic device;

associating the location metadata with the plurality of features;

communicating over a network, to a localization service:

information about the plurality of features, and

the location metadata associated with the plurality of features;

receiving, from the localization service, at least one transform relating a local coordinate frame of the portable electronic device to a second coordinate frame; and

rendering virtual content using the at least one transform, wherein rendering the virtual content using the at least one transform causes the virtual content to be positioned and/or oriented in the local coordinate frame such that the users of the plurality of portable electronic devices share a same relative position and/or orientation of the virtual content with respect to the physical environment.

10. The portable electronic device of claim 9 , wherein the location metadata includes prior localization information.

11. The portable electronic device of claim 9 , wherein:

the portable electronic device comprises an operating system with a geo-location application programming interface (API); and

the computer-executable instructions further comprise instructions for obtaining the location metadata by calling the geo-location API.

12. The portable electronic device of claim 9 , wherein:

the computer-executable instructions further comprise instructions for receiving a description of virtual content in a location based on the second coordinate frame and rendering the virtual content based on the at least one transform.

13. The portable electronic device of claim 9 , wherein:

the portable electronic device comprises a buffer configured to store a plurality of data structures; and

associating the location metadata with the plurality of features comprises storing, in a data structure of the plurality of data structures, in the buffer, the location metadata, the information about the plurality of features, and the position information for the plurality of features.

14. The portable electronic device of claim 13 , wherein:

communicating over the network to the localization service comprises transmitting the plurality of data structures from the buffer as a localization request.

15. The portable electronic device of claim 9 , wherein:

the computer-executable instructions further comprise instructions for:

constructing a local device map from the plurality of images,

defining persistent locations in the local device map, and

associating geo-location information with the persistent locations; and

associating the location metadata with the plurality of features comprises accessing, from the local device map, geo-location information associated with a persistent location of the persistent locations proximate to the portable electronic device coincident in time with capture of the plurality of images from which the plurality of features were extracted.

16. The portable electronic device of claim 9 , wherein the computer-executable instructions comprise instructions for generating or receiving geolocation information on a current location of the portable electronic device in the 3D environment.

17. A cross reality system for providing a shared experience of virtual content to users of a plurality of portable electronic devices, the system comprising:

a computing device configured to communicate with a plurality of devices through a communication network, the computing device comprising:

at least one processor configured to:

receive, from the plurality of portable electronic devices:

information about features in images captured by the plurality of portable electronic devices; and

location metadata associated with the features;

determine, using the information about the features in images captured by the plurality of portable electronic devices and the location metadata associated with the features, at least one transform for each of the plurality of portable electronic devices to obtain a plurality of transforms;

transmit, through the communication network, the plurality of transforms to respective ones of the plurality of portable electronic devices, wherein the plurality of transforms, when used to render virtual content by the respective portable electronic devices, cause users of the plurality of portable electronic devices to experience a same relative location and/or orientation of the virtual content.

18. The cross reality system of claim 17 , wherein determining, using the information about the features in the images captured by the plurality of portable electronic devices and the location metadata associated with the features, at least one transform for each of the plurality of portable electronic devices to obtain the plurality of transforms comprises:

localizing each of the plurality of portable electronic devices to a map of a set of maps; and

determining at least one transform for each of the portable electronic devices using a respective map to which the portable electronic device was localized.

19. The cross reality system of claim 17 , wherein the location metadata associated with the features comprises geo-location information from the plurality of portable electronic devices.

20. The cross reality system of claim 19 , wherein determining, using the information about the features in images captured by the plurality of portable electronic devices and the location metadata associated with the features, at least one transform for each of the plurality of portable electronic devices to obtain a plurality of transforms comprises:

querying a geo-location database based on the location metadata to identify a map of a set of maps for each of the plurality of portable electronic devices; and

determining the at least one transform for each of the plurality portable electronic devices using a map identified for the portable electronic device.

Assignments (3)
SECURITY INTEREST Recorded Oct 31, 2025
From: MAGIC LEAP, INC.; MENTOR ACQUISITION ONE, LLC; MOLECULAR IMPRINTS, INC.
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 073422/0549 →
SECURITY INTEREST Recorded Oct 28, 2025
From: MAGIC LEAP, INC.; MENTOR ACQUISITION ONE, LLC; MOLECULAR IMPRINTS, INC.
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 073388/0027 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 30, 2024
From: ZHAO, XUAN; MOORE, CHRISTIAN IVAN ROBERT; LIN, SEN; SHAHROKNI, ALI; SWAMINATHAN, ASHWIN
To: MAGIC LEAP, INC.
Reel/Frame 067271/0397 →
Continuity (3)
Continuation 17174146 · Feb 11, 2021
Provisional Application 62976154 · Feb 13, 2020
Related Publication 20240087258A1 · Mar 14, 2024
References Cited (344)
US 8243102B1 · Cornell · 2012 [cited by applicant]
US 8849957B1 · Boodman et al. · 2014 [cited by applicant]
US 9041739B2 · Latta et al. · 2015 [cited by applicant]
US 9088787B1 · Smith et al. · 2015 [cited by applicant]
US 9467718B1 · Newell et al. · 2016 [cited by applicant]
US 10192145B2 · Ben Himane et al. · 2019 [cited by applicant]
US 10335572B1 · Kumar · 2019 [cited by applicant]
US 10373366B2 · Forutanpour et al. · 2019 [cited by applicant]
US 10492981B1 · Kumar · 2019 [cited by applicant]
US 10504008B1 · Powers et al. · 2019 [cited by applicant]
US 10565731B1 · Reddy et al. · 2020 [cited by applicant]
US 10748302B1 · Dine et al. · 2020 [cited by applicant]
US 10852828B1 · Gatson et al. · 2020 [cited by applicant]
US 10854012B1 · Iyer et al. · 2020 [cited by applicant]
US 10957112B2 · Miranda et al. · 2021 [cited by applicant]
US 11201981B1 · Suiter et al. · 2021 [cited by applicant]
US 11227435B2 · Mohan et al. · 2022 [cited by applicant]
US 11232635B2 · Brodsky et al. · 2022 [cited by applicant]
US 11257294B2 · Zhao et al. · 2022 [cited by applicant]
US 11386627B2 · Caswell et al. · 2022 [cited by applicant]
US 11386629B2 · Miranda et al. · 2022 [cited by applicant]
US 11410395B2 · Velasquez et al. · 2022 [cited by applicant]
US 11551430B2 · Velasquez · 2023 [cited by examiner]
US 11562525B2 · Joseph et al. · 2023 [cited by applicant]
US 11562542B2 · Zhang et al. · 2023 [cited by applicant]
US 11568605B2 · Shahrokni et al. · 2023 [cited by applicant]
US 11632679B2 · Shveki et al. · 2023 [cited by applicant]
US 11748963B2 · Zhang et al. · 2023 [cited by applicant]
US 11789524B2 · Brodsky et al. · 2023 [cited by applicant]
US 11790619B2 · Velasquez et al. · 2023 [cited by applicant]
US 11830149B2 · Zhao · 2023 [cited by examiner]
US 11869158B2 · Caswell et al. · 2024 [cited by applicant]
US 20080303787A1 · Zheng · 2008 [cited by applicant]
US 20090031228A1 · Buchs et al. · 2009 [cited by applicant]
US 20090215536A1 · Yee et al. · 2009 [cited by applicant]
US 20090241037A1 · Hyndman · 2009 [cited by applicant]
US 20090256903A1 · Spooner et al. · 2009 [cited by applicant]
US 20100169837A1 · Hyndman · 2010 [cited by applicant]
US 20100208033A1 · Edge et al. · 2010 [cited by applicant]
US 20100257252A1 · Dougherty et al. · 2010 [cited by applicant]
US 20100287485A1 · Bertolami et al. · 2010 [cited by applicant]
US 20100321390A1 · Kim et al. · 2010 [cited by applicant]
US 20110083101A1 · Sharon et al. · 2011 [cited by applicant]
US 20110122308A1 · Duparre · 2011 [cited by applicant]
US 20110208817A1 · Toledano et al. · 2011 [cited by applicant]
US 20110254950A1 · Bibby et al. · 2011 [cited by applicant]
US 20110299736A1 · Choi et al. · 2011 [cited by applicant]
US 20120130632A1 · Bandyopadhyay et al. · 2012 [cited by applicant]
US 20120169887A1 · Zhu et al. · 2012 [cited by applicant]
US 20120188237A1 · Han et al. · 2012 [cited by applicant]
US 20120249741A1 · Maciocci et al. · 2012 [cited by applicant]
US 20120294231A1 · Finlow-Bates et al. · 2012 [cited by applicant]
US 20130002815A1 · Smoot et al. · 2013 [cited by applicant]
US 20130044128A1 · Liu et al. · 2013 [cited by applicant]
US 20130083173A1 · Geisner et al. · 2013 [cited by applicant]
US 20130141419A1 · Mount et al. · 2013 [cited by applicant]
US 20130162481A1 · Parvizi et al. · 2013 [cited by applicant]
US 20130176430A1 · Zhu et al. · 2013 [cited by applicant]
US 20130201185A1 · Kochi · 2013 [cited by applicant]
US 20130215264A1 · Soatto et al. · 2013 [cited by applicant]
US 20130222555A1 · Nagasaka et al. · 2013 [cited by applicant]
US 20130257858A1 · Na et al. · 2013 [cited by applicant]
US 20130257907A1 · Matsui · 2013 [cited by applicant]
US 20130282345A1 · McCulloch et al. · 2013 [cited by applicant]
US 20130293468A1 · Perez et al. · 2013 [cited by applicant]
US 20130321402A1 · Moore et al. · 2013 [cited by applicant]
US 20130321671A1 · Cote et al. · 2013 [cited by applicant]
US 20130321678A1 · Cote et al. · 2013 [cited by applicant]
US 20130342671A1 · Hummel et al. · 2013 [cited by applicant]
US 20140002607A1 · Shotton et al. · 2014 [cited by applicant]
US 20140003762A1 · Macnamara · 2014 [cited by applicant]
US 20140010407A1 · Sinha et al. · 2014 [cited by applicant]
US 20140097329A1 · Wadsworth · 2014 [cited by applicant]
US 20140119602A1 · Zuo · 2014 [cited by applicant]
US 20140137100A1 · Won et al. · 2014 [cited by applicant]
US 20140211855A1 · Alipour Kashi et al. · 2014 [cited by applicant]
US 20140254936A1 · Sun et al. · 2014 [cited by applicant]
US 20140254942A1 · Liu et al. · 2014 [cited by applicant]
US 20140267234A1 · Hook et al. · 2014 [cited by applicant]
US 20140282162A1 · Fein et al. · 2014 [cited by applicant]
US 20140289607A1 · Ko et al. · 2014 [cited by applicant]
US 20140306866A1 · Miller et al. · 2014 [cited by applicant]
US 20140315570A1 · Yun et al. · 2014 [cited by applicant]
US 20140368645A1 · Ahuja et al. · 2014 [cited by applicant]
US 20140372957A1 · Keane et al. · 2014 [cited by applicant]
US 20140375688A1 · Redmann et al. · 2014 [cited by applicant]
US 20150016777A1 · Abovitz et al. · 2015 [cited by applicant]
US 20150049004A1 · Deering et al. · 2015 [cited by applicant]
US 20150049201A1 · Liu et al. · 2015 [cited by applicant]
US 20150071524A1 · Lee · 2015 [cited by applicant]
US 20150126223A1 · Lee et al. · 2015 [cited by applicant]
US 20150161476A1 · Kurz et al. · 2015 [cited by applicant]
US 20150178939A1 · Bradski et al. · 2015 [cited by applicant]
US 20150186745A1 · Martini · 2015 [cited by applicant]
US 20150187133A1 · Martini · 2015 [cited by applicant]
US 20150205126A1 · Schowengerdt · 2015 [cited by applicant]
US 20150235447A1 · Abovitz et al. · 2015 [cited by applicant]
US 20150279081A1 · Monk et al. · 2015 [cited by applicant]
US 20150281869A1 · Ramachandran et al. · 2015 [cited by applicant]
US 20150302642A1 · Miller · 2015 [cited by applicant]
US 20150302652A1 · Miller et al. · 2015 [cited by applicant]
US 20150302656A1 · Miller et al. · 2015 [cited by applicant]
US 20150302664A1 · Miller · 2015 [cited by applicant]
US 20150302665A1 · Miller · 2015 [cited by applicant]
US 20150309264A1 · Abovitz et al. · 2015 [cited by applicant]
US 20150310664A1 · Boussard et al. · 2015 [cited by applicant]
US 20150321103A1 · Barnett et al. · 2015 [cited by applicant]
US 20160005229A1 · Lee et al. · 2016 [cited by applicant]
US 20160012643A1 · Kezele et al. · 2016 [cited by applicant]
US 20160026253A1 · Bradski et al. · 2016 [cited by applicant]
US 20160071278A1 · Leonard et al. · 2016 [cited by applicant]
US 20160086381A1 · Jung et al. · 2016 [cited by applicant]
US 20160147408A1 · Bevis et al. · 2016 [cited by applicant]
US 20160148433A1 · Petrovskaya et al. · 2016 [cited by applicant]
US 20160154821A1 · Kansal et al. · 2016 [cited by applicant]
US 20160179830A1 · Schmalstieg et al. · 2016 [cited by applicant]
US 20160180593A1 · Yang · 2016 [cited by applicant]
US 20160180602A1 · Fuchs · 2016 [cited by applicant]
US 20160196692A1 · Kjallstrom et al. · 2016 [cited by applicant]
US 20160217614A1 · Kraver et al. · 2016 [cited by applicant]
US 20160219408A1 · Yang et al. · 2016 [cited by applicant]
US 20160284314A1 · Darshan et al. · 2016 [cited by applicant]
US 20160300389A1 · Glenn, III et al. · 2016 [cited by applicant]
US 20160335275A1 · Williams et al. · 2016 [cited by applicant]
US 20160343165A1 · Park et al. · 2016 [cited by applicant]
US 20160358383A1 · Gauglitz et al. · 2016 [cited by applicant]
US 20160360111A1 · Thivent et al. · 2016 [cited by applicant]
US 20160370971A1 · Hackett et al. · 2016 [cited by applicant]
US 20160381118A1 · Andrews et al. · 2016 [cited by applicant]
US 20170031160A1 · Popovich et al. · 2017 [cited by applicant]
US 20170061696A1 · Li et al. · 2017 [cited by applicant]
US 20170076408A1 · D'Souza et al. · 2017 [cited by applicant]
US 20170091996A1 · Wei et al. · 2017 [cited by applicant]
US 20170094227A1 · Williams et al. · 2017 [cited by applicant]
US 20170134909A1 · Gu et al. · 2017 [cited by applicant]
US 20170185823A1 · Gold et al. · 2017 [cited by applicant]
US 20170192515A1 · Menadeva et al. · 2017 [cited by applicant]
US 20170195564A1 · Appia et al. · 2017 [cited by applicant]
US 20170208109A1 · Akselrod et al. · 2017 [cited by applicant]
US 20170236037A1 · Rhoads et al. · 2017 [cited by applicant]
US 20170237789A1 · Harner et al. · 2017 [cited by applicant]
US 20170243352A1 · Kutliroff et al. · 2017 [cited by applicant]
US 20170270713A1 · Dooley et al. · 2017 [cited by applicant]
US 20170336511A1 · Nerurkar et al. · 2017 [cited by applicant]
US 20170345167A1 · Ard et al. · 2017 [cited by applicant]
US 20170345215A1 · Khedkar et al. · 2017 [cited by applicant]
US 20170352192A1 · Petrovskaya et al. · 2017 [cited by applicant]
US 20170358140A1 · Kohler et al. · 2017 [cited by applicant]
US 20170371024A1 · Ivanov et al. · 2017 [cited by applicant]
US 20180000547A1 · Kang et al. · 2018 [cited by applicant]
US 20180012074A1 · Holz et al. · 2018 [cited by applicant]
US 20180045963A1 · Hoover et al. · 2018 [cited by applicant]
US 20180053284A1 · Rodriguez et al. · 2018 [cited by applicant]
US 20180053315A1 · Ard et al. · 2018 [cited by applicant]
US 20180082156A1 · Jin et al. · 2018 [cited by applicant]
US 20180089834A1 · Spizhevoy et al. · 2018 [cited by applicant]
US 20180114065A1 · Zeng · 2018 [cited by applicant]
US 20180122143A1 · Ellwood, Jr. · 2018 [cited by applicant]
US 20180164877A1 · Miller et al. · 2018 [cited by applicant]
US 20180189556A1 · Shamir et al. · 2018 [cited by applicant]
US 20180213359A1 · Reinhardt et al. · 2018 [cited by applicant]
US 20180218222A1 · Alrabeiah et al. · 2018 [cited by applicant]
US 20180245927A1 · Frish et al. · 2018 [cited by applicant]
US 20180261012A1 · Mullins et al. · 2018 [cited by applicant]
US 20180268237A1 · Stanimirovic et al. · 2018 [cited by applicant]
US 20180268582A1 · Schneider et al. · 2018 [cited by applicant]
US 20180268611A1 · Nourai et al. · 2018 [cited by applicant]
US 20180284802A1 · Tsai et al. · 2018 [cited by applicant]
US 20180285052A1 · Eade et al. · 2018 [cited by applicant]
US 20180286116A1 · Babu · 2018 [cited by applicant]
US 20180293771A1 · Piemonte et al. · 2018 [cited by applicant]
US 20180304153A1 · Hohjoh et al. · 2018 [cited by applicant]
US 20180307303A1 · Powderly et al. · 2018 [cited by applicant]
US 20180308377A1 · Pena-Rios et al. · 2018 [cited by applicant]
US 20180315248A1 · Bastov et al. · 2018 [cited by applicant]
US 20190005725A1 · Oonishi · 2019 [cited by applicant]
US 20190027267A1 · Hayashi et al. · 2019 [cited by applicant]
US 20190035047A1 · Lim et al. · 2019 [cited by applicant]
US 20190065814A1 · Morein et al. · 2019 [cited by applicant]
US 20190080467A1 · Hirzer et al. · 2019 [cited by applicant]
US 20190114798A1 · Afrouzi et al. · 2019 [cited by applicant]
US 20190114802A1 · Lazarow · 2019 [cited by applicant]
US 20190147341A1 · Rabinovich et al. · 2019 [cited by applicant]
US 20190188474A1 · Zahnert et al. · 2019 [cited by applicant]
US 20190197785A1 · Tate-Gans et al. · 2019 [cited by applicant]
US 20190199882A1 · Han · 2019 [cited by applicant]
US 20190206258A1 · Chang et al. · 2019 [cited by applicant]
US 20190236797A1 · Thyagharajan et al. · 2019 [cited by applicant]
US 20190287311A1 · Bhatnagar et al. · 2019 [cited by applicant]
US 20190301873A1 · Prasser et al. · 2019 [cited by applicant]
US 20190310761A1 · Agarawala et al. · 2019 [cited by applicant]
US 20190313059A1 · Agarawala et al. · 2019 [cited by applicant]
US 20190340831A1 · Scarfone et al. · 2019 [cited by applicant]
US 20190355169A1 · Sapienza et al. · 2019 [cited by applicant]
US 20190362546A1 · Wayenberg · 2019 [cited by applicant]
US 20190384379A1 · Huh · 2019 [cited by applicant]
US 20190385370A1 · Boyapalle et al. · 2019 [cited by applicant]
US 20190388182A1 · Kumar et al. · 2019 [cited by applicant]
US 20200005486A1 · Sinha et al. · 2020 [cited by applicant]
US 20200033463A1 · Lee et al. · 2020 [cited by applicant]
US 20200034624A1 · Sharma et al. · 2020 [cited by applicant]
US 20200051328A1 · Mohan et al. · 2020 [cited by applicant]
US 20200066050A1 · Ha et al. · 2020 [cited by applicant]
US 20200074739A1 · Stauber et al. · 2020 [cited by applicant]
US 20200090407A1 · Miranda et al. · 2020 [cited by applicant]
US 20200097770A1 · Sommer et al. · 2020 [cited by applicant]
US 20200111255A1 · Brodsky et al. · 2020 [cited by applicant]
US 20200126252A1 · Iyer et al. · 2020 [cited by applicant]
US 20200126256A1 · Sinha et al. · 2020 [cited by applicant]
US 20200126309A1 · Moroze et al. · 2020 [cited by applicant]
US 20200134366A1 · Xu et al. · 2020 [cited by applicant]
US 20200175766A1 · Gawrys et al. · 2020 [cited by applicant]
US 20200177870A1 · Tadi et al. · 2020 [cited by applicant]
US 20200211286A1 · Kelsey et al. · 2020 [cited by applicant]
US 20200211290A1 · Choi et al. · 2020 [cited by applicant]
US 20200252233A1 · O'Keeffe · 2020 [cited by applicant]
US 20200342670A1 · Nattinger et al. · 2020 [cited by applicant]
US 20200364901A1 · Choudhuri et al. · 2020 [cited by applicant]
US 20200364937A1 · Selbrede · 2020 [cited by applicant]
US 20200372672A1 · Schonberger et al. · 2020 [cited by applicant]
US 20200380263A1 · Yang et al. · 2020 [cited by applicant]
US 20200380769A1 · Liu et al. · 2020 [cited by applicant]
US 20200394012A1 · Wright, Jr. et al. · 2020 [cited by applicant]
US 20200401617A1 · Spiegel et al. · 2020 [cited by applicant]
US 20210019909A1 · Wang et al. · 2021 [cited by applicant]
US 20210049360A1 · Yildiz et al. · 2021 [cited by applicant]
US 20210065455A1 · Beith et al. · 2021 [cited by applicant]
US 20210074072A1 · Desai et al. · 2021 [cited by applicant]
US 20210103449A1 · Terpstra et al. · 2021 [cited by applicant]
US 20210105340A1 · Grozdanov et al. · 2021 [cited by applicant]
US 20210110614A1 · Shahrokni et al. · 2021 [cited by applicant]
US 20210110615A1 · Zhao et al. · 2021 [cited by applicant]
US 20210112427A1 · Shveki et al. · 2021 [cited by applicant]
US 20210125414A1 · Berkebile · 2021 [cited by applicant]
US 20210134064A1 · Shahrokni et al. · 2021 [cited by applicant]
US 20210142580A1 · Caswell et al. · 2021 [cited by applicant]
US 20210174596A1 · Zhang et al. · 2021 [cited by applicant]
US 20210209859A1 · Miranda et al. · 2021 [cited by applicant]
US 20210256755A1 · Joseph et al. · 2021 [cited by applicant]
US 20210256766A1 · Muhlethaler et al. · 2021 [cited by applicant]
US 20210256767A1 · Velasquez et al. · 2021 [cited by applicant]
US 20210256768A1 · Zhao et al. · 2021 [cited by applicant]
US 20210264620A1 · Ramasamy et al. · 2021 [cited by applicant]
US 20210264685A1 · Velasquez et al. · 2021 [cited by applicant]
US 20210295266A1 · McKee et al. · 2021 [cited by applicant]
US 20210315464A1 · Sol I Caros et al. · 2021 [cited by applicant]
US 20210343087A1 · Gomez Gonzalez et al. · 2021 [cited by applicant]
US 20210358150A1 · Lin et al. · 2021 [cited by applicant]
US 20220036648A1 · Wang · 2022 [cited by applicant]
US 20220092852A1 · Mohan et al. · 2022 [cited by applicant]
US 20220101607A1 · Brodsky et al. · 2022 [cited by applicant]
US 20220130120A1 · Zhao et al. · 2022 [cited by applicant]
US 20220292789A1 · Caswell et al. · 2022 [cited by applicant]
US 20220358733A1 · Velasquez et al. · 2022 [cited by applicant]
US 20230119217A1 · Velasquez et al. · 2023 [cited by applicant]
US 20230119305A1 · Zhang et al. · 2023 [cited by applicant]
US 20230127303A1 · Shahrokni et al. · 2023 [cited by applicant]
US 20230209373A1 · Shveki et al. · 2023 [cited by applicant]
US 20230222731A1 · Joseph et al. · 2023 [cited by applicant]
US 20240029369A1 · Zhang et al. · 2024 [cited by applicant]
CA 2788836A1 · 2011 [cited by applicant]
CN 102216957A · 2011 [cited by applicant]
CN 103460255A · 2013 [cited by applicant]
CN 106663411A · 2017 [cited by applicant]
CN 106937531A · 2017 [cited by applicant]
CN 112074876A · 2020 [cited by applicant]
EP 2359333A1 · 2011 [cited by applicant]
EP 2808842B1 · 2017 [cited by applicant]
EP 4046139A1 · 2022 [cited by applicant]
JP 2013141049A · 2013 [cited by applicant]
JP 2015079490A · 2015 [cited by applicant]
JP 2016522463A · 2016 [cited by applicant]
JP 2017107604A · 2017 [cited by applicant]
JP 2017529635A · 2017 [cited by applicant]
JP 2021528781A · 2021 [cited by applicant]
WO WO2012126500A1 · 2012 [cited by applicant]
WO WO2014164901A1 · 2014 [cited by applicant]
WO WO2015161307A1 · 2015 [cited by applicant]
WO WO2015192117A1 · 2015 [cited by applicant]
WO WO2016077798A1 · 2016 [cited by applicant]
WO WO2017136833A1 · 2017 [cited by applicant]
WO WO2018125428A1 · 2018 [cited by applicant]
WO WO2019046774A1 · 2019 [cited by applicant]
WO WO2019221800A1 · 2019 [cited by applicant]
Extended European Search Report dated Jan. 4, 2024 in connection with European Application No. 21753910.5. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2021/017616, mailed Jun. 24, 2021. [cited by applicant]
[No Author Listed], Axis-angle representation—Wikipedia. 6 pages. Last edited on Feb. 2, 2022. URL:https://en.wikipedia.org/wiki/Axis%E2%80%93angle_representation [retrieved on Feb. 28, 2022]. [cited by applicant]
[No Author Listed], Code release for “learning to find good correspondences” CVPR 2018. GitHub. Sep. 30, 2020. 4 pages. URL:https://github.com/vgc-uvic/learned-correspondence-release [retrieved on Feb. 22, 2022]. [cited by applicant]
[No Author Listed], Combain Location API—API Reference. 14 pages. URL:https://combain.com/api/#combain-location-api [retrieved on Feb. 24, 2021]. [cited by applicant]
[No Author Listed], Content Persistence Fundamentals. Magic Leap, Inc. Oct. 23, 2019. URL:https://developer.magicleap.com/en-us/learn/guides/content-persistence-fundamentals [retrieved on Dec. 31, 2020]. 5 pages. [cited by applicant]
[No Author Listed], Course (navigation)—Wikipedia. 3 pages. Last edited on Nov. 4, 2021. URL:https://en.wikipedia.org/wiki/Course_(navigation) [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], Geohash a Ing/at coordinate using hibert space filling curves. GitHub. Apr. 1, 2020. 9 pages. URL:https://github.com/tammoippen/geohash-hilbert [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], GitHub—gmplot/gmplot: Plot data on Google Maps, the easy way. Oct. 14, 2021. 2 pages. URL:https://github.com/vgm64/gmplot [retrieved on Feb. 28, 2022]. [cited by applicant]
[No Author Listed], Haversine formula—Wikipedia. 5 pages. Last edited on Jan. 11, 2022. URL:https://en.wikipedia.org/wiki/Haversine_formula [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], Kismet (software). Wikipedia. Last edited on Jan. 27, 2021. 3 pages. URL:https://en.wikipedia.org/wiki/Kismet_(software) [retrieved on Feb. 24, 2021]. [cited by applicant]
[No Author Listed], Points of the Compass—Wikipedia. Last edited on Dec. 4, 2021. 16 pages. URL:https://en.wikipedia.org/wiki/Points_of_the_compass [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], Progressive Web Apps. Google Developers. 2022, 5 pages. URL:https://web.dev/progressive-web-apps [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], S2 Geometry. 3 pages. URL:http://s2geometry.io/ [retrieved on Feb. 24, 2022]. [cited by applicant]
[No. Author Listed], Skid (aerodynamics)—Wikipedia. 2 pages. Last edited on Jun. 17, 2020. URL:https://en.wikipedia.org/wiki/Skid_(aerodynamic) [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], sklearn.neighbors.BallTree. 2022. 4 pages. URL:https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], Slip (aerodynamics)—Wikipedia. 5 pages. Last edited on Aug. 22, 2021. URL:https://en.wikipedia.org/wiki/Slip_(aerodynamic) [retrieved on Feb. 24, 2022]. [cited by applicant]
[No Author Listed], The difference between virtual reality, Augmented Reality and Mixed Reality. Forbes. Feb. 2, 2018. 5 pages. URL:https://www.forbes.com/sites/quora/2018/02/02/the-difference-between-virtual-reality-au… [cited by applicant]
[No Author Listed], Wi-Fi Location-Based Services 4.1 Design Guide. Jan. 30, 2014. 11 pages. URL:https://www.cisco.com/c/en/us/td/docs/solutions/Enterprise/Mobility/WiFiLBS-DG/wifich2.html. [cited by applicant]
[No Author Listed], WiGLE: Wireless Network Mapping. 2021. 2 pages. URL:https://wigle.net [retrieved on Feb. 24, 2021]. [cited by applicant]
[No Author Listed], Wind Triangle—Wikipedia. 2 pages. Last edited on Nov. 16, 2021. URL:https://en.wikipedia.org/wiki/Wind_triangle [retrieved on Feb. 24, 2022]. [cited by applicant]
Balntas et al., HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017. pp. 5173-5182. [cited by applicant]
Bansal et al., Blur image detection using Laplacian operator and Open-CV. 2016 International Conference System Modeling & Advancement in Research Trends (SMART). Nov. 2016, pp. 63-67. [cited by applicant]
Bleser et al., Cognitive learning, monitoring and assistance of industrial workflows using egocentric sensor networks. PloS one. Jun. 30, 2015;10(6):e0127769. 41 pages. [cited by applicant]
Brachmann et al., Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses. arXiv:1905.04132v2. Jul. 31, 2019. 17 pages. [cited by applicant]
Brief, Mobile Image Blur Detection with Machine Learning. May 17, 2019. 14 pages. URL:https://medium.com/snapaddy-tech-blog/mobile-image-blur-detection-with-machine-learning-c0b703eab7de. [cited by applicant]
Brodsky et al., Rendering Location Specific Virtual Content In Any Location, U.S. Appl. No. 18/460,873, filed Sep. 5, 2023. [cited by applicant]
Cadena et al., Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age. IEEE Transactions on Robotics. Dec. 2016;32(6):1309-1332. [cited by applicant]
Caswell et al., Cross Reality System With Localization Service And Shared Location-Based Content, U.S. Appl. No. 18/496,407, filed Oct. 27, 2023. [cited by applicant]
Chatterjee, How to Leverage Geolocation Capabilities to Deliver a Top Notch Mobile App. Jul. 19, 2017. 5 pages. URL:https://www.fingent.com/blog/how-to-leverage-geo-location-capabilities-to-deliver-a-top-notch-mobile-ap… [cited by applicant]
Dang et al., Eigendecomposition-free training of deep networks with zero eigenvalue-based losses. arXiv:1803.08071. Mar. 26, 2018. 25 pages. [cited by applicant]
Gidaris et al, Unsupervised representation learning by predicting image rotations. arXiv:1803.07728. Mar. 21, 2018. 16 pages. [cited by applicant]
Gomez Gonzalez et al., Cross Reality System For Large Scale Environments, U.S. Appl. No. 18/396,682, filed Dec. 26, 2023. [cited by applicant]
Henniges, Current approaches of Wifi Positioning. Service-Centric Networking Seminar. WS2011/2012. 8 pages. [cited by applicant]
Henry et al., RGB-D mapping: Using Kinect-style depth cameras for dense 30 modeling of indoor environments. The International Journal of Robotics Research. Feb. 10, 2012. 26 pages. URL:http://citeseerx.ist.psu.edu/viewd… [cited by applicant]
Huang et al., Efficient, Generalized Indoor WiFi GraphSLAM. IEEE International Conference on Robotics and Automation. 2011. 3 pages. doi: 10.1109/ICRA.2011.5979643. [cited by applicant]
Ito et al., W-RGB-D: Floor-Plan-Based Indoor Global Localization Using a Depth Camera and WiFi. 2014 IEEE international conference on robotics and automation (ICRA). May 2014, pp. 417-422. [cited by applicant]
Kurz et al., Representative feature descriptor sets for robust handheld camera localization. 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Nov. 5, 2012. pp. 65-70. [cited by applicant]
Larsson et al., Fine-grained segmentation networks: Self-supervised segmentation for improved long-term visual localization. arXiv:1908.06387v1. Aug. 18, 2019. 13 pages. [cited by applicant]
Lynen et al., Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization. Robotics: Science and Systems. Jul. 2015. 10 pages. [cited by applicant]
Lynen et al., Large-scale, real-time visual-inertial localization revisited. arXiv preprint arXiv:1907.00338v1. Jun. 30, 2019. 21 pages. [cited by applicant]
Mirowski et al., Depth camera SLAM on a low-cost WiFi mapping robot. Apr. 2012. 7 pages. doi:10.1109/TePRA.2012.6215673. [cited by applicant]
Mohanna et al., Optimization of MUSIC algorithm for angle of arrival estimation in wireless communications. NRIAG Journal of Astronomy and Geophysics. Feb. 2013:116-124. [cited by applicant]
Mueller, Fast In-memory spatial radius queries with Python. Aug. 9, 2017. 8 pages. URL:https://medium.com/@alexander.mueller/experiments-with-in-memory-spatial-radius-queries-in-python-e40c9e66cf63 [retrieved on Feb. 24… [cited by applicant]
Panzarino, What exactly WiFiSLAM is, and why Apple acquired it. Mar. 25, 2013. URL:https://thenextweb.com/apple/2013/03/26/what-exactly-wifislam-is-and-why-apple-acquired-it [retrieved Feb. 24, 2021]. [cited by applicant]
Pertuz et al., Analysis of focus measure operators for shape-from-focus. Pattern Recognition. May 2013;46:1415-32. [cited by applicant]
Qi et al., Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv:1612.00593. Apr. 10, 2017. 19 pages. [cited by applicant]
Rabinovich et al., Lumin OS & Lumin SDK: past, present and future. Magic Leap, Inc. Apr. 2, 2019. URL:https://www.magicleap.com/en-us/news/product-updates/lumin-os-and-lumin-sdk-update [retrieved on Dec. 31, 2020]. 9 pa… [cited by applicant]
Stobing, How to Add Website Links to the Windows 10 Start Menu. Howtogeek.com. 2016. 9 pages. URL:https://www.howtogeek.com/237951/how-to-add-website-links-to-the-windows-10-start-menu [Last accessed Jul. 11, 2022]. [cited by applicant]
Sturari et al., Robust and affordable retail customer profiling by vision and radio beacon sensor fusion. Pattern Recognition Letters. Oct. 1, 2016;81:30-40. [cited by applicant]
Sweeney et al., Efficient computation of absolute pose for gravity-aware augmented reality. 2015 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Sep. 29, 2015, pp. 19-24. [cited by applicant]
Taira et al., InLoc: Indoor visual localization with dense matching and view synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. pp. 7199-7209. [cited by applicant]
Tang, Applying Deep Learning to Detect Blurry Images. Dec. 12, 2017. 6 pages. URL:https://tangming2008.github.io/neural network/tensor flow/classification/Applying-Deep-Learning-to-Detect-Blurry-Images/. [cited by applicant]
Velasquez et al., Cross Reality System With Accurate Shared Maps, U.S. Appl. No. 18/457,314, filed Aug. 28, 2023. [cited by applicant]
Vogt, Real-Time Augmented Reality for Image-Guided Interventions. Doctoral dissertation, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Feb. 18, 2009. 48 pages. URL:https://opus4.kobv.de/opus4-fau/files/1235/s… [cited by applicant]
Wikidan61, Normalization (image processing). Wikipedia. Apr. 23, 2018. 2 pages. URL:https://en.wikipedia.org/w/index.php?title=Normalization_(image_processing)&oldid=837856854 [retrieved on Jun. 16, 2023]. [cited by applicant]
Willaredt, WiFi and Cell-ID based positioning—Protocols, Standards and Solutions. SNET Project WT. Jan. 26, 2011. 10 pages. [cited by applicant]
Xiong et al., A Diversified Generative Latent Variable Model for WiFi-SLAM. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Feb. 2017;31:3841-3847. [cited by applicant]
Yi et al., Learning to find good correspondences. arXiv:1711.05971. May 21, 2018. 13 pages. [cited by applicant]
Japanese Office Action dated Nov. 15, 2024 in connection with Japanese Application No. 2022-548663. [cited by applicant]