**FAST-LIO** (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package address many key issues:
1. Fast iterated Kalman filter for odometry optimization;
2. Automaticaly initialized at most steady environments;
3. Parallel KD-Tree Search to decrease the computation;
4. Robust feature extraction;
5. Surpports for different FOV.
To know more about the details, please refer to our related paper:)
**Our related paper**: our related papers are now available on arxiv:
- If you want to change the frame rate, please modify the **publish_freq** parameter in the [livox_lidar_msg.launch](https://github.com/Livox-SDK/livox_ros_driver/blob/master/livox_ros_driver/launch/livox_lidar_msg.launch) of [Livox-ros-driver](https://github.com/Livox-SDK/livox_ros_driver) before make the livox_ros_driver pakage.
Download [avia_indoor_quick_shake_example1](https://drive.google.com/file/d/1SWmrwlUD5FlyA-bTr1rakIYx1GxS4xNl/view?usp=sharing) or [avia_indoor_quick_shake_example2](https://drive.google.com/file/d/1wD485CIbzZlNs4z8e20Dv2Q1q-7Gv_AT/view?usp=sharing) and then
In order to validate the robustness and computational efficiency of FAST-LIO in actual mobile robots, we build a small-scale quadrotor which can carry a Livox Avia LiDAR with 70 degree FoV and a DJI Manifold 2-C onboard computer with a 1.8 GHz Intel i7-8550U CPU and 8 G RAM, as shown in below.
Thanks for LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), [Livox_Mapping](https://github.com/Livox-SDK/livox_mapping) and [Loam_Livox](https://github.com/hku-mars/loam_livox).
