IP Library Granted Patent US 12,643,044
Granted Patent B2
US 12,643,044 · App. 18/620,402 · Granted Jun 2, 2026

Merging local maps from mapping devices

Inventors: Anvith Ekkati (Mountain View, CA); Purna Sowmya Munukutla (Santa Clara, CA); Dharini Krishna (Santa Clara, CA); Peter James Turner (Redwood City, CA); Gandeevan Raghuraman (Santa Clara, CA); Si ying Diana Hu (Mountain View, CA)
Assignee: Niantic Spatial, Inc.
A63F13/5378A63F13/213A63F13/216A63F13/65G06T15/00G06T19/006A63F2300/8082
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Quick Facts
Patent No.
US 12,643,044
App. No.
18/620,402
Granted
Jun 2, 2026
Kind
B2
Abstract

An augmented reality system generates computer-mediated reality on a client device. The client device has sensors including a camera configured to capture image data of an environment. The augmented reality system generates a first 3D map of the environment around the client device based on captured image data. The server receives image data captured from a second client device in the environment and generates a second 3D map of the environment. The server links the first and second 3D together in a singular 3D map. The singular 3D map may be a graphical representation of the real world using nodes that represent 3D maps generated by image data captured at client devices and edges that represent transformations between the nodes.

Claims (51)

1 . A computer-implemented method of combining map data to generate a three-dimensional (3D) map of an environment, the computer-implemented method comprising:

synchronizing locations between a first client device and a second client device based on a common feature in (a) a first set of image data generated by the first client device and (b) a second set of image data generated by the second client device, wherein synchronizing the locations between the first client device and the second client device based on the common feature is performed prior to generating a first 3D map based on the first set of image data and prior to generating a second 3D map based on the second set of image data;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the first 3D map based on the first set of image data generated by the first client device;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the second 3D map based on the second set of image data generated by the second client device; and

generating a singular 3D map from the first and second 3D maps based on the synchronized locations between the first client device and the second client device.

2 . The computer-implemented method of claim 1 , wherein generating the singular 3D map comprises stitching together the first and second 3D maps based on the synchronized locations between the first client device and the second client device.

3 . The computer-implemented method of claim 1 , wherein generating the singular 3D map is further based on the common feature in the first and second sets of image data.

4 . The computer-implemented method of claim 3 , wherein the common feature is a QR code or a third client device.

5 . The computer-implemented method of claim 1 , further comprising:

before generating the singular 3D map from the first and second 3D maps, identifying a first feature in the first 3D map;

identifying a second feature in the second 3D map different than the first feature; and

determining a location of the first feature relative to the second feature based on the synchronized locations between the first client device and the second client device.

6 . The computer-implemented method of claim 5 , wherein generating the singular 3D map is further based on the location of the first feature relative to the second feature.

7 . The computer-implemented method of claim 5 , wherein the first feature is a common feature in the first and second 3D maps.

8 . The computer-implemented method of claim 1 , wherein the first and second 3D maps do not overlap.

9 . The computer-implemented method of claim 1 , wherein:

the first 3D map is associated with a first node of a graph;

the second 3D map is associated with a second node of the graph; and

the first node and the second node are linked by an edge determined based on the synchronized locations between the first client device and the second client device.

10 . The computer-implemented method of claim 1 , further comprising:

before generating the singular 3D map from the first and second 3D maps,

receiving the first set of image data and the second set of image data;

generating the first 3D map based on the first set of image data; and

generating the second 3D map based on the second set of image data.

11 . A non-transitory computer-readable storage medium storing instructions that, when executed by a computing device, cause the computing device to perform operations including:

synchronizing locations between a first client device and a second client device based on a common feature in (a) a first set of image data generated by the first client device and (b) a second set of image data generated by the second client device, wherein synchronizing the locations between the first client device and the second client device based on the common feature is performed prior to generating a first 3D map based on the first set of image data and prior to generating a second 3D map based on the second set of image data;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the a first 3D map based on the first set of image data generated by the first client device;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the second 3D map based on the second set of image data generated by the second client device; and

generating a singular 3D map from the first and second 3D maps based on the synchronized locations between the first client device and the second client device.

12 . The non-transitory computer-readable storage medium of claim 11 , wherein generating the singular 3D map comprises stitching together the first and second 3D maps based on the synchronized locations between the first client device and the second client device.

13 . The non-transitory computer-readable storage medium of claim 11 , wherein generating the singular 3D map is further based on the common feature in the first and second sets of image data.

14 . The non-transitory computer-readable storage medium of claim 13 , wherein the common feature is a QR code or a third client device.

15 . The non-transitory computer-readable storage medium of claim 11 , wherein the operations further comprise:

before generating the singular 3D map from the first and second 3D maps,

identifying a first feature in the first 3D map;

identifying a second feature in the second 3D map; and

determining a location of the first feature relative to the second feature based on the synchronized locations between the first client device and the second client device.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein generating the singular 3D map is further based on the location of the first feature relative to the second feature.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the first feature is a common feature in the first and second 3D maps.

18 . The non-transitory computer-readable storage medium of claim 11 , wherein the first and second 3D maps do not overlap.

19 . The non-transitory computer-readable storage medium of claim 11 , wherein:

the first 3D map is associated with a first node of a graph;

the second 3D map is associated with a second node of the graph; and

the first node and the second node are linked by an edge determined based on the synchronized locations between the first client device and the second client device.

20 . A computer system comprising:

a computer processor system; and

a computer-readable storage medium storing instructions that when executed by the computer processor system perform operations comprising:

synchronizing locations between a first client device and a second client device based on a common feature in (a) a first set of image data generated by the first client device and (b) a second set of image data generated by the second client device, wherein synchronizing the locations between the first client device and the second client device based on the common feature is performed prior to generating a first 3D map based on the first set of image data and prior to generating a second 3D map based on the second set of image data;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the first 3D map based on the first set of image data generated by the first client device;

responsive to synchronizing the locations between the first client device and the second client device based on the common feature, generating the second 3D map based on the second set of image data generated by the second client device; and

generating a singular 3D map from the first and second 3D maps based on the synchronized locations between the first client device and the second client device.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 16, 2025
From: NIANTIC, INC.
To: NIANTIC SPATIAL, INC.
Reel/Frame 071555/0833 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 7, 2024
From: EKKATI, ANVITH; MUNUKUTLA, PURNA SOWMYA; KRISHNA, DHARINI; TURNER, PETER JAMES; RAGHURAMAN, GANDEEVAN; HU, SI YING DIANA
To: NIANTIC, INC.
Reel/Frame 067330/0112 →
Continuity (3)
Continuation 17127199 · Dec 18, 2020
Provisional Application 62952036 · Dec 20, 2019
Related Publication 20240238675A1 · Jul 18, 2024
References Cited (58)
US 9581431B1 · Sieracki · 2017 [cited by examiner]
US 9984499B1 · Jurgenson et al. · 2018 [cited by applicant]
US 10282574B1 · Tyagi · 2019 [cited by examiner]
US 10290049B1 · Xu · 2019 [cited by examiner]
US 10547974B1 · Pollefeys · 2020 [cited by examiner]
US 10748302B1 · Dine · 2020 [cited by examiner]
US 10867408B1 · Sethi · 2020 [cited by examiner]
US 10990829B2 · Cameron · 2021 [cited by examiner]
US 11087479B1 · Geraghty et al. · 2021 [cited by applicant]
US 11321929B2 · Huo · 2022 [cited by examiner]
US 11348320B2 · Peri · 2022 [cited by examiner]
US 11969651B2 · Ekkati · 2024 [cited by examiner]
US 20120249544A1 · Maciocci et al. · 2012 [cited by applicant]
US 20120306850A1 · Balan et al. · 2012 [cited by applicant]
US 20140267234A1 · Hook · 2014 [cited by examiner]
US 20140323148A1 · Schmalstieg et al. · 2014 [cited by applicant]
US 20150199852A1 · Chang et al. · 2015 [cited by applicant]
US 20150237481A1 · Ben-Moshe · 2015 [cited by examiner]
US 20160088287A1 · Sadi et al. · 2016 [cited by applicant]
US 20160179830A1 · Schmalstieg · 2016 [cited by examiner]
US 20170287191A1 · Zawada et al. · 2017 [cited by applicant]
US 20170345167A1 · Ard · 2017 [cited by examiner]
US 20190012840A1 · Finman · 2019 [cited by examiner]
US 20190156577A1 · Barros · 2019 [cited by examiner]
US 20190156579A1 · Sheftel · 2019 [cited by examiner]
US 20190311214A1 · Lakemond · 2019 [cited by examiner]
US 20190332115A1 · Lim et al. · 2019 [cited by applicant]
US 20200050208A1 · Frick · 2020 [cited by examiner]
US 20200175760A1 · Guo et al. · 2020 [cited by applicant]
US 20200372673A1 · Iyer et al. · 2020 [cited by applicant]
US 20210360368A1 · Li · 2021 [cited by examiner]
CN 106595659A · 2017 [cited by applicant]
CN 107004028A · 2017 [cited by applicant]
CN 108369743A · 2018 [cited by applicant]
CN 108682027A · 2018 [cited by applicant]
CN 109426333A · 2019 [cited by applicant]
CN 110199313A · 2019 [cited by applicant]
CN 110322511A · 2019 [cited by applicant]
JP 07271434A · 1995 [cited by applicant]
JP 2018084699A · 2018 [cited by applicant]
TW 201528227A · 2015 [cited by applicant]
WO WO2019010466A1 · 2019 [cited by applicant]
WO WO2019230169A1 · 2021 [cited by applicant]
Bonanni, T.M., et al. “3-d map merging on pose graphs,” IEEE Robotics and Automation Letters, Jan. 18, 2017, vol. 2, Issue 2, pp. 1031-1038. [cited by applicant]
Bosse, M., et al., “Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework,” The International Journal of Robotics Research, vol. 23, No. 12, Dec. 2004, pp. 1113-1139. [cited by applicant]
Eade, E., et al., “Monocular SLAM as a Graph of Coalesced Observations,” 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp. 1-8. [cited by applicant]
Egodagamage, R., et al., “A Collaborative Augmented Reality Framework Based on Distributed Visual Slam,” 2017 International Conference on Cyberworlds, Sep. 20, 2017, pp. 25-32. [cited by applicant]
European Patent Office, Extended European Search Report and Opinion, European Patent Application No. 20901073.5, Dec. 15, 2022, 15 pages. [cited by applicant]
Japan Patent Office, Office Action, Japanese Patent Application No. 2022-538069, Oct. 31, 2023, four pages. [cited by applicant]
Kim, B., et al., “Multiple Relative Pose Graphs for Robust Cooperative Mapping,” IEEE International Conference on Robotics and Automation (ICRA), May 2010, pp. 3185-3192. [cited by applicant]
PCT International Search Report and Written Opinion, PCT Application No. PCT/IB2020/062241, Mar. 29, 2021, nine pages. [cited by applicant]
Strasdat, H. et al., “Double window optimization for constant time visual SLAM,” In 2011 IEEE International Conference on Computer Vision, Nov. 6, 2011, pp. 2352-2359. [cited by applicant]
Taiwan Intellectual Property Administration, Office Action, TW Patent Application No. 109145173, Dec. 2, 2021, 17 pages. [cited by applicant]
Taiwan Intellectual Property Office, Office Action, TW Patent Application No. 111127787, Sep. 4, 2023, 13 pages. [cited by applicant]
United States Office Action, U.S. Appl. No. 17/127,199, filed Nov. 29, 2022, 12 pages. [cited by applicant]
United States Office Action, U.S. Appl. No. 17/127,199, filed Sep. 7, 2023, 13 pages. [cited by applicant]
China National Intellectual Property Administration, Office Action, CN Application No. 202080097057.1 , Mar. 28, 2025, 17 pages. [cited by applicant]
Japanese Patent Office, Office Action, JP Patent Application No. 2024-059577, Mar. 4, 2025, 10 pages. [cited by applicant]