Semantic Segmentation-Based Enhancement of Visual SLAM Loop Closure Detection in Dynamic Indoor Environments

Current visual SLAM loop closure detection algorithms encounter significant challenges in dynamic environments, where moving objects such as pedestrians lead to inconsistencies in feature points, compromising map accuracy. This study proposes a novel visual SLAM loop closure detection algorithm leve

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.4108/eai.21-11-2024.2354632
Primary Topic
Robotics and Sensor-Based Localization
Type
article
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article

Semantic Segmentation-Based Enhancement of Visual SLAM Loop Closure Detection in Dynamic Indoor Environments

Lu Wang, Xiaoxia Lu, Chao Hu
Robotics and Sensor-Based Localization
article

Semantic Segmentation-Based Enhancement of Visual SLAM Loop Closure Detection in Dynamic Indoor Environments

Lu Wang, Xiaoxia Lu, Chao Hu
article en

Abstract

Current visual SLAM loop closure detection algorithms encounter significant challenges in dynamic environments, where moving objects such as pedestrians lead to inconsistencies in feature points, compromising map accuracy. This study proposes a novel visual SLAM loop closure detection algorithm leve

Openalex Percentile: Top 93%
Robotics and Sensor-Based Localization
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Semantic Segmentation-Based Enhancement of Visual SLAM Loop Closure Detection in Dynamic Indoor Environments — Lu Wang, Xiaoxia Lu, et al. · (2025) | TGRS Research Map | TGRS