公开(公告)号：US11313684B2

公开(公告)日：2022-04-26

申请号：US16089322

申请日：2017-03-28

Applicant: SRI INTERNATIONAL

Inventor： Han-Pang Chiu ,  Supun Samarasekera ,  Rakesh Kumar ,  Mikhail Sizintsev ,  Xun Zhou ,  Philip Miller ,  Glenn Murray

IPC: G01C21/32 ,  G01C21/34 ,  G06V10/75 ,  G06V20/10 ,  G06K9/62 ,  H04W4/021

Abstract: During GPS-denied/restricted navigation, images proximate a platform device are captured using a camera, and corresponding motion measurements of the platform device are captured using an IMU device. Features of a current frame of the images captured are extracted. Extracted features are matched and feature information between consecutive frames is tracked. The extracted features are compared to previously stored, geo-referenced visual features from a plurality of platform devices. If one of the extracted features does not match a geo-referenced visual feature, a pose is determined for the platform device using IMU measurements propagated from a previous pose and relative motion information between consecutive frames, which is determined using the tracked feature information. If at least one of the extracted features matches a geo-referenced visual feature, a pose is determined for the platform device using location information associated with the matched, geo-referenced visual feature and relative motion information between consecutive frames.

公开(公告)号：US20240403649A1

公开(公告)日：2024-12-05

申请号：US18520800

申请日：2023-11-28

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Yi Yao ,  Zachary Seymour ,  Alex Krasner ,  Bradley J. Clymer ,  Michael A. Cogswell ,  Cecile Eliane Jeannine Mackay ,  Alex C. Tozzo ,  Tixiao Shan ,  Philip Miller ,  Chuanyong Gan ,  Glenn A. Murray ,  Richard Louis Ferranti ,  Uma Rajendran ,  Supun Samarasekera ,  Rakesh Kumar ,  James Smith

IPC: G06N3/0895

Abstract: In an example, a system includes processing circuitry in communication with storage media. The processing circuitry is configured to execute a machine learning system including at least a first module, a second module and a third module. The machine learning system is configured to train one or more machine learning models. The first module is configured to generate augmented input data based on the streaming input data. The second module includes a machine learning model configured to perform a specific task based at least in part on the augmented input data. The third module configured to adapt a network architecture of the one or more machine learning models based on changes in the streaming input data.

公开(公告)号：US12062186B2

公开(公告)日：2024-08-13

申请号：US17496403

申请日：2021-10-07

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Junjiao Tian ,  Zachary Seymour ,  Niluthpol C. Mithun ,  Alex Krasner ,  Mikhail Sizintsev ,  Abhinav Rajvanshi ,  Kevin Kaighn ,  Philip Miller ,  Ryan Villamil ,  Supun Samarasekera

IPC: G06T7/174 ,  G06T3/40 ,  G06T7/38 ,  G06T7/50

CPC classification number: G06T7/174 ,  G06T3/40 ,  G06T7/38 ,  G06T7/50 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20112

Abstract: A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.