Sri International

Niluthpol C Mithun

Supun Samarasekera

Han-Pang Chiu

Zachary Seymour

Rakesh Kumar

Karan Sikka

Yi Yao

Mikhail Sizintsev

Ryan Villamil

Jonathan D Brookshire

Philip Miller

Abhinav Rajvanshi

Alex Krasner

Kowshik Thopalli

Kevin Kaighn

Qiao Wang

Junjiao Tian

Muhammad Zubair Irshad

A method, apparatus and system for object detection in sensor data having at least two modalities using a common embedding space includes creating first modality vector representations of features of sensor data having a first modality and second modality vector representations of features of sensor data having a second modality, projecting the first and second modality vector representations into the common embedding space such that related embedded modality vectors are closer together in the common embedding space than unrelated modality vectors, combining the projected first and second modality vector representations, and determining a similarity between the combined modality vector representations and respective embedded vector representations of features of objects in the common embedding space to identify at least one object depicted by the captured sensor data. In some instances, data manipulation of the method, apparatus and system can be guided by physics properties of a sensor and/or sensor data.

Physics-guided deep multimodal embeddings for task-specific data exploitation

A method, apparatus and system for efficient navigation in a navigation space includes determining semantic features and respective 3D positional information of the semantic features for scenes of captured image content and depth-related content in the navigation space, combining information of the determined semantic features of the scene with respective 3D positional information using neural networks to determine an intermediate representation of the scene which provides information regarding positions of the semantic features in the scene and spatial relationships among the semantic features, and using the information regarding the positions of the semantic features and the spatial relationships among the semantic features in a machine learning process to provide at least one of a navigation path in the navigation space, a model of the navigation space, and an explanation of a navigation action by the single, mobile agent in the navigation space.

System and method for efficient visual navigation

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.

RGBD video semantic segmentation with temporal and geometric consistency

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.

Artificial intelligence-based hierarchical planning for manned/unmanned platforms

A method, apparatus and system for visual localization includes extracting appearance features of an image, extracting semantic features of the image, fusing the extracted appearance features and semantic features, pooling and projecting the fused features into a semantic embedding space having been trained using fused appearance and semantic features of images having known locations, computing a similarity measure between the projected fused features and embedded, fused appearance and semantic features of images, and predicting a location of the image associated with the projected, fused features. An image can include at least one image from a plurality of modalities such as a Light Detection and Ranging image, a Radio Detection and Ranging image, or a 3D Computer Aided Design modeling image, and an image from a different sensor, such as an RGB image sensor, captured from a same geo-location, which is used to determine the semantic features of the multi-modal image.

Semantically-aware image-based visual localization

Growing community of inventors

Lawrenceville, NJ, United States of America

Niluthpol C Mithun