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Tanya Raghuvanshi

Bio: Tanya Raghuvanshi is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Monocular vision. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Proceedings ArticleDOI
TL;DR: An Autonomous Machine Vision system which grasps a textureless object from a clutter in a single plane, rearranges it for proper placement and then places it using vision using a unique vision-based pose estimation algorithm, collision free path planning and dynamic Change-Over algorithm for final placement.
Abstract: This paper proposes an Autonomous Machine Vision system which grasps a textureless object from a clutter in a single plane, rearranges it for proper placement and then places it using vision. It contributes to a unique vision-based pose estimation algorithm, collision free path planning and dynamic Change-Over algorithm for final placement.

3 citations


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Journal ArticleDOI
TL;DR: A comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects is presented in this article , where object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture and rigidity, which can classify objects into conventional or challenging objects.
Abstract: This paper presents a comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects. We review both object-oriented and scene-oriented aspects, using the DRVGD for unknown objects as a guide. Object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture, and rigidity, which can classify objects into conventional or challenging objects. Scene-oriented DRVGD focuses on unstructured scenes, which are explored in two aspects based on the position relationships of object-to-object, grasping isolated or stacked objects in unstructured scenes. In addition, this paper provides a detailed review of associated grasping representations and datasets. Finally, the challenges of DRVGD and future directions are pointed out.

2 citations

Proceedings ArticleDOI
13 Jul 2016
TL;DR: This proposed system develops 3D environment model utilizing mono-vision system, which is developed through capturing multiple shots from different locations and updated continuously based on the changes in the environment and the location of the robot.
Abstract: Mobile robot system will be an important asset in our future. Mobile robot not only has to execute predefined tasks programmed with, but also it must explore the unknown environment that might be pushed to work in. In this paper we propose, implement and test a new model for mobile robot environment using mono-vision system. This proposed system develops 3D environment model utilizing mono-vision system. The model is developed through capturing multiple shots from different locations. The 3D model describes the distance and the angle of objects with respect to the robot. Finally, the mobile robot will utilize this model to navigate its environment. This is achieved through projecting the 3D model into the motion floor and identifying the obstacles surrounding the robot. Then, the robot will avoid any object in its motion line. Most importantly, the model is updated continuously based on the changes in the environment and the location of the robot.

1 citations

Book ChapterDOI
01 Jan 2022