US 12,230,026 B2
Mapping objects using unmanned aerial vehicle data in GPS-denied environments
Amir H. Behzadan, College Station, TX (US)
Assigned to The Texas A&M University System, College Station, TX (US)
Appl. No. 17/782,925
Filed by The Texas A&M University System, College Station, TX (US)
PCT Filed Dec. 6, 2020, PCT No. PCT/US2020/063517
§ 371(c)(1), (2) Date Jun. 6, 2022,
PCT Pub. No. WO2021/113789, PCT Pub. Date Jun. 10, 2021.
Claims priority of provisional application 62/944,980, filed on Dec. 6, 2019.
Prior Publication US 2023/0029573 A1, Feb. 2, 2023
Int. Cl. G06V 20/13 (2022.01); B64C 39/02 (2023.01); B64U 101/30 (2023.01); G06T 3/04 (2024.01); G06T 7/73 (2017.01); G06V 10/46 (2022.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/17 (2022.01)
CPC G06V 20/13 (2022.01) [B64C 39/024 (2013.01); G06T 3/04 (2024.01); G06T 7/74 (2017.01); G06V 10/751 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/17 (2022.01); B64U 2101/30 (2023.01); G06T 2207/10016 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20101 (2013.01); G06T 2207/30181 (2013.01); G06V 10/462 (2022.01); G06V 2201/07 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A method for identifying, locating, and mapping targets of interest using UAV camera footage in GPS-denied environments, the method comprising:
(A) obtaining UAV visual data, wherein the UAV visual data comprises aerial video footage comprising a plurality of frames, wherein one of the plurality of frames comprises at least four manually-selected reference points, wherein the at least four manually-selected reference points comprise known pixel coordinates and real-world orthogonal positions;
(B) passing the UAV visual data through a convolutional neural network (CNN) to detect targets of interest based on visual features disposed in each of the plurality of frames, wherein the detection by the CNN defines at least four new reference points for each of the remaining plurality of frames, and wherein the CNN defines pixel coordinates for the targets of interest for each of the remaining plurality of frames;
(C) applying a geometric transformation to the known and defined pixel coordinates to obtain real-world orthogonal positions; and
(D) projecting the detected targets of interest onto an orthogonal map based on the obtained real-world orthogonal positions.