IP Library Granted Patent US 12,266,054
Granted Patent B2
US 12,266,054 · App. 17/860,761 · Granted Apr 1, 2025

System and methods for improved aerial mapping with aerial vehicles

Inventors: Jonathan James Millin (Santa Clara, CA); Nicholas Pilkington (Santa Clara, CA); Devin Lane (Santa Clara, CA); Christopher Sullivan (Santa Clara, CA); Michael Winn (Santa Clara, CA)
Assignee: Drone Deploy, Inc.
G06T17/05G01C11/34G05D1/0094G05D1/689G06V20/13G08G5/0013G08G5/0021G08G5/0034G08G5/006G08G5/0069G08G5/0086B64U2101/32
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 12,266,054
App. No.
17/860,761
Granted
Apr 1, 2025
Kind
B2
Abstract

A method for image generation, preferably including: generating a set of mission parameters for a UAV mission of the UAV associated with aerial scanning of a region of interest; controlling the UAV to perform the mission; generating an image subassembly corresponding to the mission; and/or rendering the image subassembly at a display.

Claims (69)

1. A computer-implemented method comprising:

receiving a first frame of a region of interest captured by a camera of an unmanned aerial vehicle (UAV) in association with a first camera pose;

receiving a second frame of the region of interest captured by the camera of the UAV in association with a second camera pose;

matching a feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on the first camera pose and the second camera pose;

generating an orthomosaic using the first frame and the second frame, based on matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame; and

performing a bundle adjustment operation to reduce a reprojection error associated with a physical feature position using only frames that are within a temporal window corresponding to a limited number of frames that are most recently captured or processed.

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

determining the first camera pose corresponding to the first frame;

determining the second camera pose corresponding to the second frame;

comparing the first camera pose and the second camera pose; and

determining motion information indicating motion between the first camera pose and the second camera pose, based on comparing the first camera pose and the second camera pose,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame based on the motion information.

3. The computer-implemented method of claim 2 , wherein the determining the first camera pose comprises determining the first camera pose based on one or more of: first global positioning system (GPS) information, first pitch information, first roll information, or first yaw information of the camera corresponding to the first frame,

wherein the determining the second camera pose comprises determining the second camera pose based on one or more of: second GPS information, second pitch information, second roll information, or second yaw information of the camera corresponding to the second frame.

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

determining a first photo subset of the first frame that depicts the feature; and

determining a second photo subset of the second frame that depicts the feature,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on determining the first photo subset and determining the second photo subset.

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

performing a triangulation operation to determine a physical feature position of the feature in a three-dimensional space.

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

providing the orthomosaic to an electronic device of a user to render the region of interest in near-real time at a display of the electronic device.

7. A device comprising:

a memory configured to store instructions; and

a processor configured to execute the instructions to perform operations comprising:

receiving a first frame of a region of interest captured by a camera of an unmanned aerial vehicle (UAV) in association with a first camera pose;

receiving a second frame of the region of interest captured by the camera of the UAV in association with a second camera pose;

matching a feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on the first camera pose and the second camera pose;

generating an orthomosaic using the first frame and the second frame, based on matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame; and

performing a bundle adjustment operation to reduce a reprojection error associated with a physical feature position using only frames that are within a temporal window corresponding to a limited number of frames that are most recently captured or processed.

8. The device of claim 7 , wherein the operations further comprise:

determining the first camera pose corresponding to the first frame;

determining the second camera pose corresponding to the second frame;

comparing the first camera pose and the second camera pose; and

determining motion information indicating motion between the first camera pose and the second camera pose, based on comparing the first camera pose and the second camera pose,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame based on the motion information.

9. The device of claim 8 , wherein the determining the first camera pose comprises determining the first camera pose based on one or more of: first global positioning system (GPS) information, first pitch information, first roll information, or first yaw information of the camera corresponding to the first frame, and

wherein the determining the second camera pose comprises determining the second camera pose based on one or more of: second GPS information, second pitch information, second roll information, or second yaw information of the camera corresponding to the second frame.

10. The device of claim 7 , wherein the operations further comprise:

determining a first photo subset of the first frame that depicts the feature; and

determining a second photo subset of the second frame that depicts the feature,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on determining the first photo subset and determining the second photo subset.

11. The device of claim 7 , wherein the operations further comprise:

performing a triangulation operation to determine a physical feature position of the feature in a three-dimensional space.

12. The device of claim 7 , wherein the operations further comprise:

providing the orthomosaic to an electronic device of a user to render the region of interest in near-real time at a display of the electronic device.

13. A non-transitory computer-readable medium configured to store instructions that, when executed by a processor, perform operations comprising:

receiving a first frame of a region of interest captured by a camera of an unmanned aerial vehicle (UAV) in association with a first camera pose;

receiving a second frame of the region of interest captured by the camera of the UAV in association with a second camera pose;

matching a feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on the first camera pose and the second camera pose;

generating an orthomosaic using the first frame and the second frame, based on matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame; and

performing a bundle adjustment operation to reduce a reprojection error associated with a physical feature position using only frames that are within a temporal window corresponding to a limited number of frames that are most recently captured or processed.

14. The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise:

determining the first camera pose corresponding to the first frame;

determining the second camera pose corresponding to the second frame;

comparing the first camera pose and the second camera pose; and

determining motion information indicating motion between the first camera pose and the second camera pose, based on comparing the first camera pose and the second camera pose,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame based on the motion information.

15. The non-transitory computer-readable medium of claim 14 , wherein the determining the first camera pose comprises determining the first camera pose based on one or more of: first global positioning system (GPS) information, first pitch information, first roll information, or first yaw information of the camera corresponding to the first frame, and

wherein the determining the second camera pose comprises determining the second camera pose based on one or more of: second GPS information, second pitch information, second roll information, or second yaw information of the camera corresponding to the second frame.

16. The non-transitory computer-readable medium of 13 , wherein the operations further comprise:

determining a first photo subset of the first frame that depicts the feature; and

determining a second photo subset of the second frame that depicts the feature,

wherein the matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame comprises matching the feature of the region of interest included in the first frame with the feature of the region of interest included in the second frame, based on determining the first photo subset and determining the second photo subset.

17. The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise:

performing a triangulation operation to determine a physical feature position of the feature in a three-dimensional space; and

performing a bundle adjustment operation to reduce a reprojection error associated with the physical feature position.

18. The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise:

providing the orthomosaic to an electronic device of a user to render the region of interest in near-real time at a display of the electronic device.

Assignments (4)
SECURITY INTEREST Recorded Sep 3, 2025
From: DRONEDEPLOY, INC.; STRUCTIONSITE, INC.
To: HERCULES CAPITAL, INC., AS AGENT
Reel/Frame 072147/0547 →
SECURITY INTEREST Recorded Sep 19, 2023
From: DRONEDEPLOY, INC.
To: HERCULES CAPITAL, INC., AS AGENT
Reel/Frame 064958/0425 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 19, 2022
From: MILLIN, JONATHAN JAMES; PILKINGTON, NICHOLAS; LANE, DEVIN; SULLIVAN, CHRISTOPHER; WINN, MICHAEL
To: INFATICS, INC.
Reel/Frame 060546/0073 →
CHANGE OF NAME Recorded Jul 19, 2022
From: INFATICS, INC.
To: DRONEDEPLOY, INC.
Reel/Frame 060727/0184 →
Continuity (5)
Continuation 17382747 · Jul 22, 2021
Continuation 16805415 · Feb 28, 2020
Continuation 15887832 · Feb 2, 2018
Provisional Application 62453926 · Feb 2, 2017
Related Publication 20220343599A1 · Oct 27, 2022
References Cited (74)
US 6151539A · Bergholz et al. · 2000 [cited by applicant]
US 8280633B1 · Eldering et al. · 2012 [cited by applicant]
US 8497905B2 · Nixon · 2013 [cited by applicant]
US 8626361B2 · Gerlock · 2014 [cited by applicant]
US 8897543B1 · Lin et al. · 2014 [cited by applicant]
US 8965107B1 · Schpok et al. · 2015 [cited by applicant]
US 8965598B2 · Kruglick · 2015 [cited by applicant]
US 9201424B1 · Ogale · 2015 [cited by examiner]
US 9266611B2 · Rambo · 2016 [cited by applicant]
US 9346543B2 · Kugelmass · 2016 [cited by applicant]
US 9346544B2 · Kugelmass · 2016 [cited by applicant]
US 9352833B2 · Kruglick · 2016 [cited by applicant]
US 9651920B2 · Jang et al. · 2017 [cited by applicant]
US 9836885B1 · Eraker · 2017 [cited by examiner]
US 10339639B2 · Christ et al. · 2019 [cited by applicant]
US 10515458B1 · Yakimenko et al. · 2019 [cited by applicant]
US 20010038718A1 · Kumar · 2001 [cited by examiner]
US 20020120474A1 · Hele et al. · 2002 [cited by applicant]
US 20030218674A1 · Zhao et al. · 2003 [cited by applicant]
US 20040139470A1 · Treharne · 2004 [cited by applicant]
US 20050197981A1 · Bingham · 2005 [cited by examiner]
US 20060239537A1 · Shragai et al. · 2006 [cited by applicant]
US 20090003691A1 · Padfield et al. · 2009 [cited by applicant]
US 20090012995A1 · Sartor et al. · 2009 [cited by applicant]
US 20090110241A1 · Takemoto et al. · 2009 [cited by applicant]
US 20090110267A1 · Zakhor · 2009 [cited by examiner]
US 20100004802A1 · Bodin et al. · 2010 [cited by applicant]
US 20110064312A1 · Janky et al. · 2011 [cited by applicant]
US 20110228047A1 · Markham et al. · 2011 [cited by applicant]
US 20110270484A1 · Grube · 2011 [cited by applicant]
US 20120038770A1 · Azulai et al. · 2012 [cited by applicant]
US 20120314068A1 · Schultz · 2012 [cited by applicant]
US 20130004017A1 · Medasani et al. · 2013 [cited by applicant]
US 20130052994A1 · Hayward et al. · 2013 [cited by applicant]
US 20130235199A1 · Nixon · 2013 [cited by applicant]
US 20130282208A1 · Mendez-Rodriguez et al. · 2013 [cited by applicant]
US 20140219514A1 · Johnston et al. · 2014 [cited by applicant]
US 20140253375A1 · Rudow · 2014 [cited by examiner]
US 20140267397A1 · Wagner · 2014 [cited by examiner]
US 20140316616A1 · Kugelmass · 2014 [cited by applicant]
US 20150009206A1 · Arendash et al. · 2015 [cited by applicant]
US 20150036888A1 · Weisenburger · 2015 [cited by applicant]
US 20150070392A1 · Azulai et al. · 2015 [cited by applicant]
US 20150120878A1 · Horgan et al. · 2015 [cited by applicant]
US 20150226575A1 · Rambo · 2015 [cited by applicant]
US 20150243073A1 · Chen et al. · 2015 [cited by applicant]
US 20150248584A1 · Greveson et al. · 2015 [cited by applicant]
US 20150248759A1 · Lin et al. · 2015 [cited by applicant]
US 20150319769A1 · Grabowsky et al. · 2015 [cited by applicant]
US 20150336671A1 · Winn et al. · 2015 [cited by applicant]
US 20150367958A1 · Lapstun · 2015 [cited by examiner]
US 20160046373A1 · Kugelmass · 2016 [cited by applicant]
US 20160046374A1 · Kugelmass · 2016 [cited by applicant]
US 20160150142A1 · Lapstun et al. · 2016 [cited by applicant]
US 20170012697A1 · Gong et al. · 2017 [cited by applicant]
US 20170039765A1 · Zhou · 2017 [cited by examiner]
US 20170083024A1 · Reijersen Van Buuren · 2017 [cited by applicant]
US 20170084037A1 · Barajas Hernandez et al. · 2017 [cited by applicant]
US 20170109577A1 · Wang et al. · 2017 [cited by applicant]
US 20170138733A1 · Michiels · 2017 [cited by examiner]
US 20170178358A1 · Greveson et al. · 2017 [cited by applicant]
US 20170205826A1 · Smith et al. · 2017 [cited by applicant]
US 20170206648A1 · Marra · 2017 [cited by examiner]
US 20180356492A1 · Hamilton · 2018 [cited by applicant]
US 20190026919A1 · Aratani · 2019 [cited by applicant]
US 20190155302A1 · Lukierski et al. · 2019 [cited by applicant]
EP 3158396B1 · 2019 [cited by examiner]
DJ I Phantom 2 Vision + Training—Checklists (https:///dronelife.com/2014/10/17/ultimate-preflight-checklist-phanton2-vision) (Year 2014). [cited by applicant]
Chapter 10, Principles of Photogrammetry, Mar. 31, 1993, https://www.lpl.ariaona.edu/hamilton/sites/lpl.arizona.edu.hamilton/files/courses/ptys551/Principles_of_Photogrammetry.pdf, pp. 10.1-10-19. [cited by applicant]
International Search Report and Written Opinion of the ISA, dated Apr. 12, 2018, for application No. PCT US18/016721. [cited by applicant]
Ciresan, Dan , et al., “Multi-column Deep Neural Networks for Image Classification,” Mar. 28, 2012, pp. 1-8. [cited by applicant]
Goddemeier, Niklas , et al., “Coverage Evaluation of Wireless Networks for Unmanned Aerial Systems”, IEEE Globecom 2010 Workshop on Wireless Networking for Unmannaed Aerial Vehicles. [cited by applicant]
Laliberte, Andrea S., et al., “Acquisition Orthorectification, and Object-based Classification of Unmanned Aerial Vehicle (UAV) Imagery for Rangeland Monitoring”, Photogrammetric Engineering & Remote Sensing, Vo. 76, No… [cited by applicant]
Skaloud, J. , et al., “Exterior Orientation by Direct Measurement of Camera Position and Attitude”, International Archives of Photogrammetry and Remote Sensing, vol. XXX1, Part B3, 1996, pp. 125-130. [cited by applicant]