Enhancing Robot Self-Localization Accuracy and Robustness: A Variational Autoencoder-Based Approach

Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of...

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Bibliographic Details
Published in2023 5th International Conference on Control and Robotics (ICCR) pp. 145 - 149
Main Authors Abe, Asuma, Pongthanisorn, Goragod, Kaneko, Shin-ichiro, Capi, Genci
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.11.2023
Subjects
Estimation
indoor environments
Robot kinematics
Robot localization
Robot sensing systems
Robustness
Task analysis
Training data
Variational Autoencoder
wheelchair robot
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Summary:Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of VAE makes the system adaptive to varying environmental conditions. Our findings demonstrate that utilizing both the robot's coordinates and images as training data significantly enhances the accuracy of robot self-localization estimation and improves the robustness of the system due to sensor data noise.
DOI:10.1109/ICCR60000.2023.10444744