This paper is an overview to Visual Simultaneous Localization and Mapping (V-SLAM). We
discuss the basic definitions in the SLAM and vision system fields and provide a review of …

Simultaneous localization and mapping (SLAM) techniques are widely researched, since
they allow the simultaneous creation of a map and the sensors' pose estimation in an …

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 …

Neural implicit representations have recently shown encouraging results in various
domains, including promising progress in simultaneous localization and mapping (SLAM) …

Dynamic radiance field reconstruction methods aim to model the time-varying structure and
appearance of a dynamic scene. Existing methods, however, assume that accurate camera …

We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-
time scene reconstruction from casually taken monocular images. To achieve this, we …

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

Abstract Neural Radiance Fields (NeRF) have recently gained a surge of interest within the
computer vision community for its power to synthesize photorealistic novel views of real …

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 …

We show for the first time that a multilayer perceptron (MLP) can serve as the only scene
representation in a real-time SLAM system for a handheld RGB-D camera. Our network is …