The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2023
Session ID : 2P1-G05
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Localization via Fusion of a Deep Generative Model and a Particle Filter
*Ryoko SHIOJIMAKiyoshi IRIEYasuo HAYASHIBARA
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Abstract

The purpose of this research is to construct a probabilistic model of observation from data for the problem of self-position estimation from camera images. In this paper, we propose a method to fuse particle filters with probabilistic distribution images of the robot’s position and posture generated using a deep generative model called Conditional Variational Autoencoder(CVAE). To evaluate the effectiveness of the proposed method, experiments and evaluations were conducted on self-position estimation using single images and particle filter, respectively. As a result, the estimation error was reduced by fusing the generated image and particle filter, confirming the effectiveness of the proposed method.

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© 2023 The Japan Society of Mechanical Engineers
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