Cencosud

https://felixendres.github.io
is a state of the art SLAM system for RGB D cameras e g the Microsoft Kinect You can use it to create highly accurate 3D point clouds or OctoMaps RGBDSLAMv2 is based on the ROS

https://link.springer.com › article
In the experimental section the proposed method is integrated into ORB SLAM3 and tested using the RGB D datasets from TUM The results demonstrate that the proposed

https://cvg.cit.tum.de › research › vslam › rgbdslam
Our RGB D SLAM system builds upon our Direct RGB D Odometry see below It extends the odometry approach to include a geometric error term and perform frame to keyframe

https://www.sciencedirect.com › science › article › pii
This paper proposes a high precision RGB D SLAM system specifically designed for dynamic scenarios where semantic segmentation networks may fail The system integrates

https://arxiv.org › abs
In this paper we present 2DGS SLAM an RGB D SLAM system using 2D Gaussian splatting as the map representation By leveraging the depth consistent rendering

https://www.mdpi.com
We present SGDO SLAM a real time RGB D semantic aware SLAM framework building upon ORB SLAM2 to address non static environments Firstly a multi constraint

https://ieeexplore.ieee.org › document
In order to improve the accuracy and robustness of SLAM in complex dynamic environments based on ORB SLAM2 this article proposes a SLAM system that combines semantic

https://github.com › felixendres
RGBDSLAM v2 is a state of the art SLAM system for RGB D cameras e g the Microsoft Kinect or the Asus Xtion Pro Live You can use it to create 3D point clouds or OctoMaps

https://cvg.cit.tum.de › _media › spezial › bib
In this paper we present an approach to SLAM based on RGB D data that consists of the four processing steps illustrated in Figure 2 First we extract visual features from the incoming