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Improve generalization for neural visual-SLAM with Bayes online learning

Published online by Cambridge University Press:  31 March 2025

Jun Liu  and
Haihang Deng Open the ORCID record for Haihang Deng [Opens in a new window]
Jun Liu
Affiliation:
School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, Peoples R China
Haihang Deng*
Affiliation:
School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, Peoples R China
*
Corresponding author: Haihang Deng; Email: kuokuo980512@163.com
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Abstract

Among various deep learning-based SLAM systems, many exhibit low accuracy and inadequate generalization on non-training datasets. The deficiency in generalization ability can result in significant errors within SLAM systems during real-world applications, particularly in environments that diverge markedly from those represented in the training set. This paper presents a methodology to enhance the generalization capabilities of deep learning SLAM systems. It leverages their superior performance in feature extraction and introduces Exponential Moving Average (EMA) and Bayes online learning to improve generalization and localization accuracy in varied scenarios. Experimental validation, utilizing Absolute Trajectory Error (ATE) metrics on the dataset, has been conducted. The results demonstrate that this method effectively reduces errors by $20\%$ on the EuRoC dataset and by $35\%$ on the TUM dataset, respectively.

Keywords

deep learningSLAMEMABayes online learning

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 5 , May 2025 , pp. 1660 - 1674
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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