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Learning from Simulation to Improve Robotic Sensing

Saseendran, Amrutha (2019) Learning from Simulation to Improve Robotic Sensing. DLR-Interner Bericht. DLR-IB-RM-OP-2019-56. Master's. Universität Bremen.

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The ability of the robot to sense its environment is essential for its autonomous operation. Precise object detection and pose estimation, derived from the sensing capabilities, is crucial for the autonomy of any robotic system. Deep learning and neural networks have already revolutionized this field. The research community has successfully developed powerful deep learning algorithms using neural networks for faster and accurate object detection and pose estimation. However, these methods require a large amount of annotated dataset for the training of neural networks. Preparation of such high quality annotated dataset is expensive and time-consuming. An alternative method is to make use of the easily available simulation data instead of real data for training. Because of the domain gap between the simulation and the real data, networks trained with simulation data fails to perform well on real images. This thesis explores the possibility of generating realistic looking images using Generative adversarial network (GAN), in order to overcome the existing domain gap between the simulated and real images. Recent research on GANs has shown that these networks are capable of producing photo-realistic images and is a promising new area of the research field for producing realistic synthetic images. Inspired from one of the variants of GAN known as CycleGAN, the proposed work explores the possibility of generating realistic images from simulated images using modified CycleGAN architecture. Using the T-LESS and YCB dataset as the benchmark, the generated images are then evaluated by training an object detector network. The quality of the generated images are measured by the accuracy of the detector and then compared with the real domain images.

Item URL in elib:https://elib.dlr.de/131943/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Learning from Simulation to Improve Robotic Sensing
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:March 2019
Refereed publication:No
Open Access:Yes
Keywords:Robotic Sensing
Institution:Universität Bremen
Department:Institut für Theoretische Elektrotechnik und Mikroelektronik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Beinhofer, Gabriele
Deposited On:13 Dec 2019 10:04
Last Modified:13 Dec 2019 10:04

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