Autonomous robots require accurate localization in GPS-denied environments like indoors or urban canyons.GNSS-INS systems are prone to failure in these conditions, while real-time SLAM often drift without loop closures Map-based localization offers a stable and accurate alternative, but it faces several key challenges:
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Real-time performance and Scalability: Handling high-resolution 3D maps and computing scan-to-map registration efficiently.
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Drift correction: Fusing local motion estimation with global map constraints while preserving consistency.
This project presents a robust and real-time localization framework for GNSS-denied environments by fusing LiDAR-Inertial Odometry (FAST-LIO2) with multithreaded NDT-based map matching using a sliding-window factor graph. The system achieves centimeter- to decimeter-level accuracy across diverse datasets, maintaining low-latency performance suitable for real- world autonomous navigation.
Figure 1: Complete Diagram of The Localization System
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Figure 2: Sensor setup
Installation
cd ~/ros2_ws/src
git clone git@github.com:eliyaskidnae/Robust-LiDAR-Inertial-Re-Localization.git
cd ~/ros2_ws
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release
save the map file (map/map.pcd) to the map/ directory.
Running the system
source ~/ros2_ws/install/setup.bash
source /install/setup.bash
ros2 launch fast_lio mapping.launch.py config_file:='ouster64.yaml' #run FAST-LIO2 odometry
ros2 launch lidar_localization_ros2 lidar_localization.launch.py #run scan to map localization
ros2 launch lidar_localization_ros2 fusion.launch.py #run the fusion of FAST-LIO2 and scan to map localization
Click the image above or watch the demo on YouTube.