Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, there are still some challenges: the limited scene representation capability of implicit encodings, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a real-time dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness in dynamic scenes, a trait that sets it apart from other NeRF-based methods.
@article{arxiv.2403.11776,
title = {DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding},
author = {Wenhua Wu and Guangming Wang and Ting Deng and Sebastian Aegidius and Stuart Shanks and Valerio Modugno and Dimitrios Kanoulas and Hesheng Wang},
journal= {arXiv preprint arXiv:2403.11776},
year = {2024}
}