123 lines
5.3 KiB
Markdown
123 lines
5.3 KiB
Markdown
**Noted: Ubuntu 16.04 and lower is not supported**
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## FAST-LIO
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**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:
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1. Fast iterated Kalman filter for odometry optimization;
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2. Automaticaly initialized at most steady environments;
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3. Parallel KD-Tree Search to decrease the computation;
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## FAST-LIO 2.0 (2021-07-04 Update)
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<!--  -->
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<!-- [](https://youtu.be/2OvjGnxszf8) -->
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<div align="left">
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<img src="doc/real_experiment2.gif" width=49% />
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<img src="doc/ulhkwh_fastlio.gif" width = 49% >
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</div>
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**Related video:**
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[FAST-LIO2](https://youtu.be/2OvjGnxszf8)
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[FAST-LIO1](https://youtu.be/iYCY6T79oNU)
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**New features:**
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1. Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate.
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2. Direct odometry on Raw LiDAR points (feature extraction can be closed), achieving better accuracy.
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3. Since no need for feature extraction, FAST-LIO2 can support different LiDAR Types including spinning (Velodyne, Ouster) and solid-state (Avia, horizon) LiDARs. And most of LiDARs can be easily supported.
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4. Support external IMU.
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5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry 4B with 8G RAM.
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**Contributors**
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[Wei Xu 徐威](https://github.com/XW-HKU)，[Yixi Cai 蔡逸熙](https://github.com/Ecstasy-EC)，[Dongjiao He 贺东娇](https://github.com/Joanna-HE)，[Fangcheng Zhu 朱方程](https://github.com/zfc-zfc)，[Jiarong Lin 林家荣](https://github.com/ziv-lin)，[Zheng Liu 刘政](https://github.com/Zale-Liu)
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To know more about the details, please refer to our related paper:)
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**Related papers**:
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our related papers are now available on arxiv:
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[FAST-LIO2: Fast Direct LiDAR-inertial Odometry (Currently Uavailable)]()
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[FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://arxiv.org/abs/2010.08196)
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<!-- <div align="center">
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<img src="doc/results/HKU_HW.png" width = 49% >
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<img src="doc/results/HKU_MB_001.png" width = 49% >
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</div> -->
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## 1. Prerequisites
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### 1.1 **Ubuntu** and **ROS**
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**Ubuntu >= 18.04 (Ubuntu 16.04 is not supported)**
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ROS >= Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)
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### 1.2. **PCL && Eigen && openCV**
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PCL >= 1.8, Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).
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Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
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### 1.3. **livox_ros_driver**
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Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
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## 2. Build
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Clone the repository and catkin_make:
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```
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cd ~/catkin_ws/src
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git clone https://github.com/XW-HKU/fast_lio.git
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git submodule update --init
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cd ..
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catkin_make
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source devel/setup.bash
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```
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*Remarks:*
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- If you want to use a custom build of PCL, add the following line to ~/.bashrc
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```export PCL_ROOT={CUSTOM_PCL_PATH}```
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## 3. Directly run
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### 3.1 For Avia
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Connect to your PC to Livox Avia LiDAR by following [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then
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```
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....
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cd ~/catkin_ws
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roslaunch fast_lio mapping_avia.launch
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roslaunch livox_ros_driver livox_lidar_msg.launch
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```
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*Remarks:*
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- 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.
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## 4. Rosbag Example
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### 4.1 Indoor rosbag (Livox Avia LiDAR)
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<div align="center"><img src="doc/results/HKU_LG_Indoor.png" width=100% /></div>
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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
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```
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roslaunch fast_lio mapping_avia.launch
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rosbag play YOUR_DOWNLOADED.bag
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```
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### 4.2 Outdoor rosbag (Livox Avia LiDAR)
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<div align="center"><img src="doc/results/HKU_MB_002.png" width=100% /></div>
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<!-- <div align="center"><img src="doc/results/mid40_outdoor.png" width=90% /></div> -->
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Download [avia_hku_main building_mapping](https://drive.google.com/file/d/1GSb9eLQuwqmgI3VWSB5ApEUhOCFG_Sv5/view?usp=sharing) and then
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```
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roslaunch fast_lio mapping_avia_outdoor.launch
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rosbag play YOUR_DOWNLOADED.bag
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```
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## 5.Implementation on UAV
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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.
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<div align="center">
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<img src="doc/uav01.jpg" width=40.5% >
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<img src="doc/uav_system.png" width=57% >
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</div>
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## 6.Acknowledgments
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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).
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