Optimized ORBSLAM3 to run on TUM/EuRoc/KITTI dataset

Real-time SLAM with deep features (XFeat + ORB-SLAM3)

Creating a segmented 3D octomap of the surroundings using ORB-SLAM3, with the possibility of removing or adding specific segmented objects to the octomap.

ORB-SLAM3 for Windows Platform

A Docker image equipped with ORB-SLAM 3 and ready for dev. No more GPU, dependencies and build problems !

The easiest way to understand ORB_SLAM3 in Korean

Twilight SLAM is unique framework augmenting the SLAM navigation frameworks with low-light image enhancement modules for navigating in dusky or extremely low-light or any illumination rendered featureless environments.

ORB SLAM3 with a wrapper for ROS 1 (noetic)

在ORB-SLAM3的基础上，通过SOLO实例分割获得物体的Mask， 然后在运行时去除物体的关键点，从而达到动态鲁棒性

Learing ORB_SLAM3 step by step.

A custom-built drone package equipped with sensors including Kinect, IMU, Lidar and GPS especially made for incorporating Visual Inertial SLAM into the system.

Resources for SiTAR, a situated trajectory analysis system for AR which provides in-the-wild pose error estimates

This repository contains an example of running ORB-SLAM3 on a docker container with Ubuntu 22.04 and GUI

A step-by-step guide to installing ORB-SLAM3 on Ubuntu, designed for beginners. This repository covers all necessary dependencies, configurations, and troubleshooting tips to help you set up and run ORB-SLAM3 with ease.

A Windows compatible version of ORB-SLAM3 (https://github.com/UZ-SLAMLab/ORB_SLAM3)

Useful scripts and configs for preprocessing ORB-SLAM3 datasets

framework enhancing the robustness of visual odometry and SLAM in drone navigation through simulated urban canyon environments. Combines classical SLAM algorithms with deep learning-based feature enhancement, loop closure, and descriptor networks. Built on AirSim and ORB-SLAM3, tailored for GPS-denied, low-texture, and dynamically lit condition

Comparing machine and human perception of environment texture for AR