2024
DOI: 10.1109/access.2024.3393908
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Improving Long Term Accuracy of Visual Localization in Urban Environment

Nattee Niparnan,
Sukhum Sattaratnamai,
Attawith Sudsang

Abstract: Localization remains a pivotal aspect of mobile robotics, with robots required to discern their position by comparing sensor inputs against a pre-established environmental map. Notably, environmental shifts over time can diminish the reliability of these localization efforts. Addressing this challenge, our study introduces two interventions: dynamic object masking and CNN model fine-tuning, both scrutinized through extensive real-world experiments involving a robot operating continuously over a four-month span… Show more

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