[RAL 24] SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
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Updated
Mar 3, 2025 - C++
[RAL 24] SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
A lightweight setero visual SLAM system implementation, including complete closed-loop detection, front-end tracking, back-end optimization, visualization and other parts.
A simple Loopclosure detection for A-LOAM
Finding loop closures for a closed track for Formula Student events like FSG, FSA, FS East and many more using data-points provided by GPS.
Integrates a differential-drive mobile robot with ROS2 Humble and RTAB-Map SLAM for mapping and localization using an Intel RealSense D455 RGB-D camera. It supports simulation in Gazebo, real-time visual odometry, loop closure, and multi-session map persistence.
Implementation of visual-inertial SLAM using RealSense D455 and RTAB-Map in ROS. Compares ORB and SIFT for feature detection in real-time 3D mapping with loop closure and pose graph optimization on a mobile robot.
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