IP Library Granted Patent US 12,373,987
Granted Patent B2
US 12,373,987 · App. 18/619,622 · Granted Jul 29, 2025

Systems and methods for object identification with a calibrated stereo-vision system

Inventors: Leaf Alden Jiang (Concord, MA); Philip Bradley Rosen (Lincoln, MA); Piotr Swierczynski (Berlin, DE)
Assignee: NODAR Inc.
G06T7/85B60W40/02G06T7/579G06T7/593G06T7/90G06T7/97G06V20/582H04N13/239H04N13/243H04N13/246H04N13/254H04N13/271H04N13/282H04N17/002B60W2420/403G06T2207/10012G06T2207/10024G06T2207/10028G06T2207/30252H04N2013/0081
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Quick Facts
Patent No.
US 12,373,987
App. No.
18/619,622
Filed
Mar 28, 2024
Granted
Jul 29, 2025
Kind
B2
Art Unit
2425
USPC
348/47
Abstract

A long-baseline and long depth-range stereo vision system is provided that is suitable for use in non-rigid assemblies where relative motion between two or more cameras of the system does not degrade estimates of a depth map. The stereo vision system may include a processor that tracks camera parameters as a function of time to rectify images from the cameras even during fast and slow perturbations to camera positions. Factory calibration of the system is not needed, and manual calibration during regular operation is not needed, thus simplifying manufacturing of the system.

Claims (51)

1. A stereo-vision system for monitoring an object, the system comprising:

a first camera sensor configured to obtain a plurality of frames of a first image of a scene and to generate first sensor signals corresponding to the frames of the first image;

a second camera sensor configured to obtain a plurality of frames of a second image of the scene and to generate second sensor signals corresponding to the frames of the second image; and

at least one computer processor configured to

calibrate the first and second camera sensors by:

determining calibration parameters for each of the first and second camera sensors, the calibration parameters including extrinsic parameters and intrinsic parameters, wherein determining the calibration parameters comprises:

using a first calibration engine to determine a first set of calibration parameters comprising a subset of the extrinsic parameters, and

using a second calibration engine to determine a second set of calibration parameters comprising the subset of extrinsic parameters and at least one additional calibration parameter from among the extrinsic parameters and the intrinsic parameters; and

updating camera parameters for the first and second camera sensors using the determined calibration parameters.

2. The system of claim 1 , wherein the first and second camera sensors are separated by a baseline in a range of 1 m to 200 m.

3. The system of claim 1 , wherein:

the first and second images are color images, and

the at least one computer processor is further configured to identify the object in the first and second images by identifying a color of at least one characteristic of the object.

4. The system of claim 1 , wherein the scene comprises a scene of a construction site.

5. The system of claim 1 , wherein

the first calibration engine is configured to determine the first set of calibration parameters every first number of frames of the first and second sensor signals, and

the second calibration engine is configured to determine the second set of calibration parameters every second number of frames of the first and second sensor signals, the second number being different from the first number.

6. The system of claim 1 , wherein the first camera sensor and the second camera sensor are untethered from each other.

7. The system of claim 1 , wherein the first camera sensor and the second camera sensor are mounted on a non-rigid structure that is able to flex from environmental conditions.

8. The system of claim 1 , wherein the first camera sensor and the second camera sensor are not attached to a common rigid mounting member.

9. The system of claim 1 , wherein using the first calibration engine to determine the first set of calibration parameters comprises using the first calibration engine to determine a roll calibration parameter and a pitch calibration parameter.

10. The system of claim 1 , wherein the at least one additional calibration parameter from among the extrinsic parameters and the intrinsic parameters comprises a translation vector.

11. The system of claim 1 , wherein using the second calibration engine to determine the second set of calibration parameters comprises using the second calibration engine to determine each of the extrinsic parameters and each of the intrinsic parameters.

12. A computer-implemented calibration method performed by at least one computer processor to provide a stereo-vision system for monitoring an object, the method comprising:

receiving from a first camera sensor first sensor signals corresponding to a plurality of frames of a first image of a scene;

receiving from a second camera sensor second sensor signals corresponding to a plurality of frames of a second image of the scene; and

calibrating the first and second camera sensors by:

determining calibration parameters for each of the first and second camera sensors, the calibration parameters including extrinsic parameters and intrinsic parameters, wherein determining the calibration parameters comprises:

using a first calibration engine to determine a first set of calibration parameters comprising a subset of the extrinsic parameters, and

using a second calibration engine to determine a second set of calibration parameters comprising the subset of extrinsic parameters and at least one additional calibration parameter from among the extrinsic parameters and the intrinsic parameters; and

updating camera parameters for the first and second camera sensors using the determined calibration parameters.

13. The method of claim 12 , wherein the first and second camera sensors are separated by a baseline in a range of 1 m to 200 m.

14. The method of claim 12 , wherein the first and second camera sensors are separated by a baseline in a range of 5 m to 50 m.

15. The method of claim 12 , further comprising:

identifying the object in the first and second images by identifying a color of at least one characteristic of the object,

wherein the first and second images are color images.

16. The method of claim 12 , wherein the scene comprises a scene of a construction site.

17. The method of claim 12 , further comprising:

tracking a series of estimates of the calibration parameters over time; and

filtering the series of estimates to determine the estimate.

18. The method of claim 12 , wherein

the first calibration engine is configured to determine the first set of calibration parameters every first number of frames of the first and second sensor signals, and

the second calibration engine is configured to determine the second set of calibration parameters every second number of frames of the first and second sensor signals, the second number being different from the first number.

19. A non-transitory computer-readable storage medium storing computer-readable code that, when executed by at least one computer processor, performs a method to provide a stereo-vision system for monitoring an object, wherein the method comprises:

receiving from a first camera sensor first sensor signals corresponding to a plurality of frames of a first image of a scene;

receiving from a second camera sensor second sensor signals corresponding to a plurality of frames of a second image of the scene;

calibrating the first and second camera sensors by:

determining calibration parameters for each of the first and second camera sensors, the calibration parameters comprising extrinsic parameters and intrinsic parameters, wherein determining the calibration parameters comprises:

using a first calibration engine to determine a first set of calibration parameters comprising a subset of the extrinsic parameters, and

using a second calibration engine to determine a second set of calibration parameters comprising the subset of extrinsic parameters and at least one additional calibration parameter from among the extrinsic parameters and the intrinsic parameters; and

updating camera parameters for the first and second camera sensors using the determined calibration parameters.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 10, 2024
From: JIANG, LEAF ALDEN; ROSEN, PHILIP BRADLEY; SWIERCZYNSKI, PIOTR
To: NODAR INC.
Reel/Frame 067950/0062 →
Continuity (5)
Continuation 17693634 · Mar 14, 2022
Continuation 17365623 · Jul 1, 2021
Continuation PCTUS2021012294 · Jan 6, 2021
Provisional Application 62964148 · Jan 22, 2020
Related Publication 20240242382A1 · Jul 18, 2024
References Cited (130)
US 6392688B1 · Barman et al. · 2002 [cited by applicant]
US 8208716B2 · Choi et al. · 2012 [cited by applicant]
US 8797387B2 · Huggett et al. · 2014 [cited by applicant]
US 8971634B2 · Barnum · 2015 [cited by applicant]
US 8981966B2 · Stein et al. · 2015 [cited by applicant]
US 9286680B1 · Jiang et al. · 2016 [cited by applicant]
US 9369689B1 · Tran et al. · 2016 [cited by applicant]
US 9509979B2 · Livyatan et al. · 2016 [cited by applicant]
US 9958964B2 · Wurster · 2018 [cited by applicant]
US 10097812B2 · Livyatan et al. · 2018 [cited by applicant]
US 10244228B2 · Millett · 2019 [cited by applicant]
US 10257489B2 · Nam · 2019 [cited by applicant]
US 10269257B1 · Gohl et al. · 2019 [cited by applicant]
US 10430959B2 · Chang et al. · 2019 [cited by applicant]
US 10567748B2 · Okouneva · 2020 [cited by applicant]
US 10591594B2 · Oyaizu et al. · 2020 [cited by applicant]
US 10965929B1 · Bellows et al. · 2021 [cited by applicant]
US 11024037B2 · Du et al. · 2021 [cited by applicant]
US 11157751B2 · Kumano et al. · 2021 [cited by applicant]
US 11282234B2 · Jiang et al. · 2022 [cited by applicant]
US 11321875B2 · Jiang et al. · 2022 [cited by applicant]
US 11321876B2 · Jiang et al. · 2022 [cited by applicant]
US 11427193B2 · Jiang et al. · 2022 [cited by applicant]
US 11577748B1 · Wang et al. · 2023 [cited by applicant]
US 11983899B2 · Jiang et al. · 2024 [cited by applicant]
US 12043283B2 · Wang et al. · 2024 [cited by applicant]
US 20060140510A1 · Wallace et al. · 2006 [cited by applicant]
US 20070070069A1 · Samarasekera et al. · 2007 [cited by applicant]
US 20070291125A1 · Marquet · 2007 [cited by applicant]
US 20090195371A1 · Camus · 2009 [cited by applicant]
US 20100208034A1 · Chen · 2010 [cited by applicant]
US 20110025548A1 · Nickolaou · 2011 [cited by applicant]
US 20110050864A1 · Bond · 2011 [cited by applicant]
US 20120321172A1 · Jachalsky et al. · 2012 [cited by applicant]
US 20130063594A1 · Hwang et al. · 2013 [cited by applicant]
US 20130188018A1 · Stevens et al. · 2013 [cited by applicant]
US 20130329015A1 · Pulli et al. · 2013 [cited by applicant]
US 20140119663A1 · Barnum · 2014 [cited by applicant]
US 20140168377A1 · Cluff · 2014 [cited by examiner]
US 20140267616A1 · Krig · 2014 [cited by applicant]
US 20150103147A1 · Ho et al. · 2015 [cited by applicant]
US 20160323560A1 · Jin et al. · 2016 [cited by applicant]
US 20160323561A1 · Jin et al. · 2016 [cited by applicant]
US 20170278014A1 · Lessmann et al. · 2017 [cited by applicant]
US 20170287169A1 · Garcia · 2017 [cited by applicant]
US 20170307759A1 · Pei et al. · 2017 [cited by applicant]
US 20170358092A1 · Bliebel et al. · 2017 [cited by applicant]
US 20180007345A1 · Bougnoux · 2018 [cited by applicant]
US 20180077403A1 · Onomura · 2018 [cited by examiner]
US 20180176543A1 · Wan · 2018 [cited by applicant]
US 20180222499A1 · Gomes et al. · 2018 [cited by applicant]
US 20190028688A1 · Kumar · 2019 [cited by examiner]
US 20190087186A1 · Endo · 2019 [cited by applicant]
US 20190158813A1 · Rowell et al. · 2019 [cited by applicant]
US 20190204427A1 · Abari et al. · 2019 [cited by applicant]
US 20190208181A1 · Rowell et al. · 2019 [cited by applicant]
US 20190220989A1 · Harmsen et al. · 2019 [cited by applicant]
US 20190289282A1 · Briggs et al. · 2019 [cited by applicant]
US 20190295282A1 · Smolyanskiy et al. · 2019 [cited by applicant]
US 20190304164A1 · Zhang · 2019 [cited by applicant]
US 20200064483A1 · Li · 2020 [cited by examiner]
US 20200077073A1 · Nash et al. · 2020 [cited by applicant]
US 20200177870A1 · Tadi et al. · 2020 [cited by applicant]
US 20200346581A1 · Lawson et al. · 2020 [cited by applicant]
US 20200409376A1 · Ebrahimi Afrouzi et al. · 2020 [cited by applicant]
US 20210003683A1 · Chen et al. · 2021 [cited by applicant]
US 20210118162A1 · Fang et al. · 2021 [cited by applicant]
US 20210264175A1 · Zhang et al. · 2021 [cited by applicant]
US 20210327092A1 · Jiang et al. · 2021 [cited by applicant]
US 20210350576A1 · Jiang et al. · 2021 [cited by applicant]
US 20210352259A1 · Jiang et al. · 2021 [cited by applicant]
US 20220111839A1 · Jiang et al. · 2022 [cited by applicant]
US 20230005184A1 · Jiang et al. · 2023 [cited by applicant]
US 20230076036A1 · Jiang et al. · 2023 [cited by applicant]
US 20240183963A1 · Swierczynski et al. · 2024 [cited by applicant]
US 20240326787A1 · Jiang et al. · 2024 [cited by applicant]
DE 102012009577A1 · 2012 [cited by applicant]
EP 1457384A1 · 2004 [cited by applicant]
JP 2003269917A · 2003 [cited by applicant]
JP 2008022125A · 2008 [cited by applicant]
JP 2008509619A · 2008 [cited by applicant]
JP 2014153819A · 2014 [cited by examiner]
JP 2015136056A · 2015 [cited by applicant]
JP 2015158749A · 2015 [cited by applicant]
JP 2016048839A · 2016 [cited by applicant]
KR 1020090031998A · 2009 [cited by applicant]
WO WO2015015542A1 · 2015 [cited by applicant]
WO WO2016171050A1 · 2016 [cited by applicant]
WO WO2017057058A1 · 2017 [cited by applicant]
WO WO2017209015A1 · 2017 [cited by applicant]
WO WO2018196001A1 · 2018 [cited by applicant]
WO WO2019155719A1 · 2019 [cited by applicant]
WO WO2021150369A1 · 2021 [cited by applicant]
https://web.archive.org/web/20191017195809/https://en.wikipedia.org/wiki/Airbus_A320_family (Year: 2019). [cited by examiner]
International Search Report and Written Opinion for International Application No. PCT/US2021/012294, mailed May 3, 2021. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2021/012294, mailed Aug. 4, 2022. [cited by applicant]
Extended European Search Report for European Application No. 21744461.1, dated Jan. 19, 2024. [cited by applicant]
[No. Author Listed] NODAR Brick Demo. Vimeo. https://vimeo.com/497966938. Jan. 7, 2021:1. [cited by applicant]
Achanta et al., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. May 2, 20129;34(11):2274-82. [cited by applicant]
Adi et al., Distance measurement with a stereo camera. Int. J. Innov. Res. Adv. Eng. Nov. 2017;4(11):24-7. [cited by applicant]
Ashigahara, How close has computer vision come to human vision? Difficulties and practical approaches to avoid them. Vision Systems for robots and their applications. Journal of the Institute of Image Information and Te… [cited by applicant]
Badino et al., Free space computation using stochastic occupancy grids and dynamic programming. Workshop on Dynamical Vision, ICCV, Rio de Janeiro, Brazil. Oct. 20, 2007;20:73. [cited by applicant]
Bishop, Emerging from stealth, NODAR introduces “Hammerhead 3D Vision” platform for automated driving. Forbes. https://www.forbes.com/sites/richardbishop1/2021/01/07/emerging-from-stealth-nodar-introduces-hammerhead-3d-… [cited by applicant]
Cholakkal et al., LiDAR-Stereo Camera Fusion for Accurate Depth Estimation. 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT Automotive). Nov. 18, 2020:1-6. [cited by applicant]
Dai et al., A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles. World Electric Vehicle Journal. Sep. 2021;12(3):139. [cited by applicant]
Fan et al., Real-time stereo vision-based lane detection system. Measurement Science and Technology. May 24, 2018;29(7):074005. [cited by applicant]
Feng et al., Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Transactions on Intelligent Transportation Systems. Feb. 17, 2020;22(3):1341-60. [cited by applicant]
Guindel et al., Automatic extrinsic calibration for lidar-stereo vehicle sensor setups. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). Oct. 16, 2017:1-6. [cited by applicant]
Hamid et al., Stereo matching algorithm based on deep learning: A survey. Journal of King Saud University: Computer and Information Sciences. Aug. 28, 2020:1-11. [cited by applicant]
Hinzmann et al., Flexible stereo: Constrained, non-rigid, wide-baseline stereo vision for fixed-wing aerial platforms. 2018 IEEE International Conference on Robotics and Automation (ICRA) May 21, 2018:2550-7. [cited by applicant]
Hirschmüller et al., Evaluation of cost functions for stereo matching. 2007 IEEE Conference on Computer Vision and Pattern Recognition. Jun. 17, 2007:1-8. [cited by applicant]
Hirschmüller, Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence. Dec. 18, 2007;30(2):328-41. [cited by applicant]
Hsu et al., Online Recalibration of a Camera and Lidar System. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Oct. 7, 2018:4053-8. [cited by applicant]
Hu et al., A quantitative evaluation of confidence measures for stereo vision. IEEE Transactions on Pattern Analysis and Machine Intelligence. Jan. 31, 2012;34(11):2121-33. [cited by applicant]
John et al., Automatic calibration and registration of lidar and stereo camera without calibration objects. 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Nov. 5, 2015:231-7. [cited by applicant]
Kakegawa et al., Road surface segmentation based on vertically local disparity histogram for stereo camera. International Journal of Intelligent Transportation Systems Research. May 2018;16(2):90-7. [cited by applicant]
Longuet-Higgins, A computer algorithm for reconstructing a scene from two projections. Nature. Sep. 1981;293(5828):133-5. [cited by applicant]
Nelder et al., A simplex method for function minimization. The Computer Journal. Jan. 1, 1965;7(4):308-13. [cited by applicant]
Poggi et al., On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Apr. 2, 2021:1-8. [cited by applicant]
Rajaraman et al., Fully automatic, unified stereo camera and LiDAR-camera calibration. Automatic Target Recognition XXXI. Apr. 12, 2021;11729:270-277. [cited by applicant]
Rhemann et al., Fast cost-vol. filtering for visual correspondence and beyond. CVPR '11: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Jun. 2011:3017-24. [cited by applicant]
Rosero et al., Calibration and multi-sensor fusion for on-road obstacle detection. 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR). Nov. 8, 2017:1-6. [cited by applicant]
Sarika et al., Census filtering based stereomatching under varying radiometric conditions. Procedia Computer Science. Jan. 1, 2015;58:315-20. [cited by applicant]
Shin et al., Vision-based navigation of an unmanned surface vehicle with object detection and tracking abilities. Machine Vision and Applications. Jan. 2018;29(1):95-112. [cited by applicant]
Woo et al., Localization for autonomous driving. Handbook of Position Location: Theory, Practice, and Advances, Second Edition. Mar. 23, 2018:1051-87. [cited by applicant]
Zaarane et al., Distance measurement system for autonomous vehicles using stereo camera. Array. Mar. 1, 2020;5:100016. [cited by applicant]
Zabih et al., Non-parametric local transforms for computing visual correspondence. European Conference on Computer Vision May 2, 1994:151-158. [cited by applicant]
Zhang et al., A robust and rapid camera calibration method by one captured image. IEEE Transactions on Instrumentation and Measurement. Dec. 17, 2018;68(10):4112-21. [cited by applicant]
Extended European Search Report for European Application No. 21918078.3, dated Oct. 22, 2024. [cited by applicant]
Kemsaram et al. A stereo perception framework for autonomous vehicles. In2020 IEEE 91st vehicular technology conference (VTC2020-Spring) May 25, 2020:(pp. 1-6). [cited by applicant]
Cited By (1)
US 12,450,778