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As the fields of robotics and drone technologies are continually advancing, the challenge of teaching these agents to learn and maneuver in the real world becomes increasingly important. A critical component of this is the ability for a robot to map and understand its surrounding unknown environment, both in terms of physical structure and object classification. In this project we tackle the challenge of mapping a 3D space with annotations using only 2D images acquired from a Parrot Drone. In order to make such a system operate efficiently in close to real time, we address a number challenges including (1) creating a optimized version of Faster RCNN that can operate on drone hardware while still being accurate, (2) developing a method to reconstruct 3D spaces from 2D images annotated with bounding boxes, and (3) using generated 3D annotations to complete drone motion planning for unknown space exploration.

Pertenece a

Caltech Authors  

Autor(es)

Nair, Suraj -  Ramachandran, Anshul -  Kundzicz, Peter - 

Id.: 70413543

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Tipo de recurso: Conference or Workshop Item  -  PeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://resolver.caltech.edu/CaltechAUTHORS:20171110-152713005
[References] https://authors.library.caltech.edu/83135/

Fecha de contribución: 11-nov-2017

Contacto:

Localización:
* Nair, Suraj and Ramachandran, Anshul and Kundzicz, Peter (2017) Annotated Reconstruction of 3D Spaces Using Drones. In: MIT IEEE Undergraduate Technology Research Conference 2017, November 3-5, 2017, Cambridge, MA. (In Press) http://resolver.caltech.edu/CaltechAUTHORS:20171110-152713005

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