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公开(公告)号：US11960994B2
公开(公告)日：2024-04-16
申请号：US17151506
申请日：2021-01-18
Applicant: SRI International
Inventor： Han-Pang Chiu , Jonathan D. Brookshire , Zachary Seymour , Niluthpol C. Mithun , Supun Samarasekera , Rakesh Kumar , Qiao Wang
Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.
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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
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.
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公开(公告)号：US20200184718A1
公开(公告)日：2020-06-11
申请号：US16523313
申请日：2019-07-26
Applicant: SRI International
Inventor： Han-Pang Chiu , Supun Samarasekera , Rakesh Kumar , Bogdan C. Matei , Bhaskar Ramamurthy
Abstract: A method for providing a real time, three-dimensional (3D) navigational map for platforms includes integrating at least two sources of multi-modal and multi-dimensional platform sensor information to produce a more accurate 3D navigational map. The method receives both a 3D point cloud from a first sensor on a platform with a first modality and a 2D image from a second sensor on the platform with a second modality different from the first modality, generates a semantic label and a semantic label uncertainty associated with a first space point in the 3D point cloud, generates a semantic label and a semantic label uncertainty associated with a second space point in the 2D image, and fuses the first space semantic label and the first space semantic uncertainty with the second space semantic label and the second space semantic label uncertainty to create fused 3D spatial information to enhance the 3D navigational map.
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公开(公告)号：US20230394294A1
公开(公告)日：2023-12-07
申请号：US17151506
申请日：2021-01-18
Applicant: SRI International
Inventor： Han-Pang Chiu , Jonathan D. Brookshire , Zachary Seymour , Niluthpol C. Mithun , Supun Samarasekera , Rakesh Kumar , Qiao Wang
Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.
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公开(公告)号：US20220299592A1
公开(公告)日：2022-09-22
申请号：US17695784
申请日：2022-03-15
Applicant: SRI International
Inventor： Han-Pang Chiu , Abhinav Rajvanshi , Alex Krasner , Mikhail Sizintsev , Glenn A. Murray , Supun Samarasekera
Abstract: A method, apparatus and system for determining change in pose of a mobile device include determining from first ranging information received at a first and a second receiver on the mobile device from a stationary node during a first time instance, a distance from the stationary node to the first receiver and the second receiver, determining from second ranging information received at the first receiver and the second receiver from the stationary node during a second time instance, a distance from the stationary node to the first receiver and second receiver, and determining from the determined distances during the first time instance and the second time instance, how far and in which direction the first receiver and the second receiver moved between the first time instance and the second time instance to determine a change in pose of the mobile device, where a position of the stationary node is unknown.
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Patent Agency Ranking
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.
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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
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.
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公开(公告)号：US20220092366A1
公开(公告)日：2022-03-24
申请号：US17478177
申请日：2021-09-17
Applicant: SRI International
Inventor： Han-Pang Chiu , Junjiao Tian , Zachary Seymour , Niluthpol C. Mithun
Abstract: Techniques are disclosed for an image understanding system comprising a machine learning system that applies a machine learning model to perform image understanding of each pixel of an image, the pixel labeled with a class, to determine an estimated class to which the pixel belongs. The machine learning system determines, based on the classes with which the pixels are labeled and the estimated classes, a cross entropy loss of each class. The machine learning system determines, based on one or more region metrics, a weight for each class and applies the weight to the cross entropy loss of each class to obtain a weighted cross entropy loss. The machine learning system updates the machine learning model with the weighted cross entropy loss to improve a performance metric of the machine learning model for each class.
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公开(公告)号：US10991156B2
公开(公告)日：2021-04-27
申请号：US16523313
申请日：2019-07-26
Applicant: SRI International
Inventor： Han-Pang Chiu , Supun Samarasekera , Rakesh Kumar , Bogdan C. Matei , Bhaskar Ramamurthy
Abstract: A method for providing a real time, three-dimensional (3D) navigational map for platforms includes integrating at least two sources of multi-modal and multi-dimensional platform sensor information to produce a more accurate 3D navigational map. The method receives both a 3D point cloud from a first sensor on a platform with a first modality and a 2D image from a second sensor on the platform with a second modality different from the first modality, generates a semantic label and a semantic label uncertainty associated with a first space point in the 3D point cloud, generates a semantic label and a semantic label uncertainty associated with a second space point in the 2D image, and fuses the first space semantic label and the first space semantic uncertainty with the second space semantic label and the second space semantic label uncertainty to create fused 3D spatial information to enhance the 3D navigational map.
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公开(公告)号：US10929713B2
公开(公告)日：2021-02-23
申请号：US16163273
申请日：2018-10-17
Applicant: SRI International
Inventor： Han-Pang Chiu , Supun Samarasekera , Rakesh Kumar , Varun Murali
Abstract: Techniques are disclosed for improving navigation accuracy for a mobile platform. In one example, a navigation system comprises an image sensor that generates a plurality of images, each image comprising one or more features. A computation engine executing on one or more processors of the navigation system processes each image of the plurality of images to determine a semantic class of each feature of the one or more features of the image. The computation engine determines, for each feature of the one or more features of each image and based on the semantic class of the feature, whether to include the feature as a constraint in a navigation inference engine. The computation engine generates, based at least on features of the one or more features included as constraints in the navigation inference engine, navigation information. The computation engine outputs the navigation information to improve navigation accuracy for the mobile platform.
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