-
公开(公告)号：US20210256722A1
公开(公告)日：2021-08-19
申请号：US17171544
申请日：2021-02-09
Applicant: Raytheon Company
Inventor： Torsten A. Staab , Steven B. Seida , Jody D. Verret , Richard W. Ely , Stephen J. Raif
IPC: G06T7/593 , G06K9/00 , G01S17/894 , B64C39/02
Abstract: Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.
-
2.发明授权公开(公告)号：US09430872B2
公开(公告)日：2016-08-30
申请号：US14201008
申请日：2014-03-07
Applicant: Raytheon Company
Inventor： Stephen J. Raif , John J. Coogan , Steven B. Seida , Paul L. Pryor , Carlton E. Nance , Wyatt D. Sharp, III , Jody D. Verret
CPC classification number: G06T17/00 , G06F17/30241 , G06F17/30256 , G06T7/0002 , G06T17/05 , G06T2200/04 , G06T2207/10028 , G06T2207/30168 , G06T2210/56
Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.
Abstract translation: 从被动图像生成点云的系统和方法。 形成图像簇，其中每个图像簇包括从一组无源图像中选择的两个或更多个被动图像。 基于每个图像集群的性能预测分数，为每个图像集群预测可以从每个图像集群生成的点云的质量。 基于其性能预测分数，选择图像簇的子集用于进一步处理。 对于生成的每个点云确定特定任务质量得分，并选择具有最高质量得分的点云进行存储。
-
3.发明申请公开(公告)号：US20140253543A1
公开(公告)日：2014-09-11
申请号：US14201008
申请日：2014-03-07
Applicant: Raytheon Company
Inventor： Stephen J. Raif , John J. Coogan , Steven B. Seida , Paul L. Pryor , Carlton E. Nance , Wyatt D. Sharp, III , Jody D. Verret
IPC: G06T17/00
CPC classification number: G06T17/00 , G06F17/30241 , G06F17/30256 , G06T7/0002 , G06T17/05 , G06T2200/04 , G06T2207/10028 , G06T2207/30168 , G06T2210/56
Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.
Abstract translation: 从被动图像生成点云的系统和方法。 形成图像簇，其中每个图像簇包括从一组无源图像中选择的两个或更多个被动图像。 基于每个图像集群的性能预测分数，为每个图像集群预测可以从每个图像集群生成的点云的质量。 基于其性能预测分数，选择图像簇的子集用于进一步处理。 对于生成的每个点云确定特定任务质量得分，并选择具有最高质量得分的点云进行存储。
-
公开(公告)号：US11964762B2
公开(公告)日：2024-04-23
申请号：US17171544
申请日：2021-02-09
Applicant: Raytheon Company
Inventor： Torsten A. Staab , Steven B. Seida , Jody D. Verret , Richard W. Ely , Stephen J. Raif
IPC: B64C39/02 , B64U101/30 , G01S17/894 , G06T7/593 , G06V20/13
CPC classification number: B64C39/024 , G01S17/894 , G06T7/593 , G06V20/13 , B64U2101/30 , G06T2207/10028
Abstract: Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.
-
-
-
Search Results
4
records
(took 0.01 seconds)
