Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented
reality applications. However current methods are often hampered by the non-volumetric or …

We present the first application of 3D Gaussian Splatting in monocular SLAM the most
fundamental but the hardest setup for Visual SLAM. Our method which runs live at 3fps …

In this paper we introduce GS-SLAM that first utilizes 3D Gaussian representation in the
Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance …

Neural implicit representations have recently become popular in simultaneous localization
and mapping (SLAM), especially in dense visual SLAM. However, existing works either rely …

We propose a dense neural simultaneous localization and mapping (SLAM) approach for
monocular RGBD input which anchors the features of a neural scene representation in a …

We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that
simultaneously performs accurate semantic mapping high-quality surface reconstruction and …

The integration of neural rendering and the SLAM system recently showed promising results
in joint localization and photorealistic view reconstruction. However existing methods fully …

The capability of a mobile robot to efficiently and safely perform complex missions is limited
by its knowledge of the environment, namely the situation. Advanced reasoning, decision …

Neural implicit scene representations have recently shown encouraging results in dense
visual SLAM. However existing methods produce low-quality scene reconstruction and low …

We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting.
It incorporates appearance, geometry, and semantic features through multi-channel …