DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding
Wenhua Wu Guangming Wang Ting Deng Sebastian Aegidius Stuart Shanks Valerio Modugno Dimitrios Kanoulas Hesheng Wang
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.