Abstract: Traditional machine learning (ML) based systems used for scene recognition and object recognition have the disadvantage that they require huge quantity of labeled data to generate data models for the purpose of aiding the scene and object recognition. The disclosure herein generally relates to image processing, and, more particularly, to method and system for generating 3D mesh generation using planar and non-planar data. The system extracts planar point cloud and non-planar point cloud from each RGBD image in a sequence of RGBD images fetched as input, and then generates a planar mesh and a non-planar mesh for planar and non-planar objects in the image. A mesh representation is generated by merging the planar mesh and the non-planar mesh. Further, an incremental merging of the mesh representation is performed on the sequence of RGBD images, based on an estimated camera pose information, to generate representation of the scene.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Abstract: Traditional machine learning (ML) based systems used for scene recognition and object recognition have the disadvantage that they require huge quantity of labeled data to generate data models for the purpose of aiding the scene and object recognition. The disclosure herein generally relates to image processing, and, more particularly, to method and system for generating 3D mesh generation using planar and non-planar data. The system extracts planar point cloud and non-planar point cloud from each RGBD image in a sequence of RGBD images fetched as input, and then generates a planar mesh and a non-planar mesh for planar and non-planar objects in the image. A mesh representation is generated by merging the planar mesh and the non-planar mesh. Further, an incremental merging of the mesh representation is performed on the sequence of RGBD images, based on an estimated camera pose information, to generate representation of the scene.

Abstract: The present disclosure provides a system and a method for stitching images using non-linear optimization and multi-constraint cost function minimization. Most of conventional homography based transformation approaches for image alignment, calculate transformations based on linear algorithms which ignore parameters such as lens distortion and unable to handle parallax for non-planar images resulting in improper image stitching with misalignments. The disclosed system and the method generates initial stitched image by estimating a global homography for each image using estimated pairwise homography matrix and feature point correspondences for each pair of images, based on a non-linear optimization. Local warping based image alignment is applied on the initial stitched image, using multi-constraint cost function minimization to mitigate aberrations caused by noises in the global homography estimation to generate the refined stitched image.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Abstract: The present disclosure provides a system and a method for stitching images using non-linear optimization and multi-constraint cost function minimization. Most of conventional homography based transformation approaches for image alignment, calculate transformations based on linear algorithms which ignore parameters such as lens distortion and unable to handle parallax for non-planar images resulting in improper image stitching with misalignments. The disclosed system and the method generates initial stitched image by estimating a global homography for each image using estimated pairwise homography matrix and feature point correspondences for each pair of images, based on a non-linear optimization. Local warping based image alignment is applied on the initial stitched image, using multi-constraint cost function minimization to mitigate aberrations caused by noises in the global homography estimation to generate the refined stitched image.

Abstract: Feature based visual simultaneous localization and mapping (SLAM) do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing visual SLAMs produce partial reconstruction when the number of 3D-2D correspondences is insufficient for incremental camera estimation using bundle adjustment. Systems and methods of the present disclosure provide edge points based monocular visual SLAM that mitigates these problems. The SLAM is initialized through a validation process. A local optimization process is provided for stable pose estimation in situations where camera tracking becomes unreliable in a very low-textured challenging environment. An efficient and reliable loop closing process that uses structural properties of edges in the frames is also provided.

Abstract: Feature based visual simultaneous localization and mapping (SLAM) do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing visual SLAMs produce partial reconstruction when the number of 3D-2D correspondences is insufficient for incremental camera estimation using bundle adjustment. Systems and methods of the present disclosure provide edge points based monocular visual SLAM that mitigates these problems. The SLAM is initialized through a validation process. A local optimization process is provided for stable pose estimation in situations where camera tracking becomes unreliable in a very low-textured challenging environment. An efficient and reliable loop closing process that uses structural properties of edges in the frames is also provided.

Abstract: Integrated intelligent system adapted for any operating system and/or multi-OS computing environment seamlessly having sensory input/data acquisition cum recording server group and/or analytics server group enabling fail-safe integration and/or optimized utilization of various sensory inputs for various utility applications.

Abstract: Integrated intelligent system adapted for any operating system and/or multi-OS computing environment seamlessly having sensory input/data acquisition cum recording server group and/or analytics server group enabling fail-safe integration and/or optimized utilization of various sensory inputs for various utility applications.