add velodyne instructions

kevinlisun · Jul 5, 2021 · b98bfb3 · b98bfb3
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diff --git a/README.md b/README.md
@@ -1,12 +1,19 @@
 **Noted: Ubuntu 16.04 and lower is not supported**
 
+## Related Works
+
+1. [ikd-Tree](https://github.com/hku-mars/ikd-Tree): A state-of-art dynamic KD-Tree for 3D kNN search.
+2. [IKFOM](https://github.com/hku-mars/IKFoM): A Toolbox for fast and high-precision on-manifold Kalman filter.
+3. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
+4. [R2LIVE](https://github.com/hku-mars/dyn_small_obs_avoidance): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
+
 ## FAST-LIO
 **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;
 
-## FAST-LIO 2.0 (2021-07-04 Update)
+## FAST-LIO 2.0 (2021-07-05 Update)
 <!-- ![image](doc/real_experiment2.gif) -->
 <!-- [![Watch the video](doc/real_exp_2.png)](https://youtu.be/2OvjGnxszf8) -->
 <div align="left">
@@ -23,21 +30,18 @@
 **New features:**
 1. Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate.
 2. Direct odometry on Raw LiDAR points (feature extraction can be closed), achieving better accuracy.
-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.
+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 can be easily extended to support more LiDAR.
 4. Support external IMU.
 5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry 4B with 8G RAM.
 
+
 **Contributors**
 
 [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)
 
-To know more about the details, please refer to our related paper:)
-
 **Related papers**: 
 
-our related papers are now available on arxiv:
-
-[FAST-LIO2: Fast Direct LiDAR-inertial Odometry (Currently Uavailable)]()
+[FAST-LIO2: Fast Direct LiDAR-inertial Odometry]() (Currently Unavailable)
 
 [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://arxiv.org/abs/2010.08196)
 
@@ -51,6 +55,8 @@ our related papers are now available on arxiv:
 ### 1.1 **Ubuntu** and **ROS**
 **Ubuntu >= 18.04 (Ubuntu 16.04 is not supported)**
 
+For **Ubuntu 18.04 or higher**, the **default** PCL and Eigen is enough for FAST-LIO to work normally.
+
 ROS    >= Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)
 
 ### 1.2. **PCL && Eigen && openCV**
@@ -61,33 +67,61 @@ Eigen  >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.ph
 ### 1.3. **livox_ros_driver**
 Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
 
+*Remarks:*
+- Since the FAST-LIO must support Livox serials LiDAR firstly, so the **livox_ros_driver** must be installed and **sourced** before run any FAST-LIO luanch file.
+- How to source? The easiest way is add the line ``` source $Licox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Licox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document).
+
+
 ## 2. Build
 Clone the repository and catkin_make:
 
 ```
-    cd ~/catkin_ws/src
-    git clone https://github.com/XW-HKU/fast_lio.git
+    cd ~/$A_ROS_DIR$/src
+    git clone https://github.com/hku-mars/FAST_LIO.git
+    cd FAST_LIO
     git submodule update --init
-    cd ..
+    cd ../..
     catkin_make
     source devel/setup.bash
 ```
-*Remarks:*
+- Remember to source the livox_ros_driver before build (follow 1.3 **livox_ros_driver**)
 - If you want to use a custom build of PCL, add the following line to ~/.bashrc
 ```export PCL_ROOT={CUSTOM_PCL_PATH}```
 ## 3. Directly run
 ### 3.1 For Avia
 Connect to your PC to Livox Avia LiDAR by following  [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then
 ```
-    ....
-    cd ~/catkin_ws
+    cd ~/$FAST_LIO_ROS_DIR$
+    source devel/setup.bash
     roslaunch fast_lio mapping_avia.launch
     roslaunch livox_ros_driver livox_lidar_msg.launch
-    
 ```
-*Remarks:*
 - 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.
 
+### 3.2 For Velodyne or Ouster (using Velodyne as an example)
+
+Step A: Setup before run
+
+Edit ``` FAST-LIO/config/velodyne.yaml ``` to set the below parameters:
+
+1. lidar point cloud topic name: ``` lid_topic ```
+2. IMU topic name: ``` imu_topic ```
+3. Line number (we tested 16 and 32 line, but not tested 64 or above): ``` scan_line ```
+4. Translational extrinsic: ``` extrinsic_T ```
+5. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
+- The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).
+
+Step B: Run below
+```
+    cd ~/$FAST_LIO_ROS_DIR$
+    source devel/setup.bash
+    roslaunch fast_lio mapping_velodyne.launch
+```
+
+Step C: Run LiDAR's ros driver or play rosbag.
+*Remarks:*
+- We will produce some velodyne datasets which is already transfered to Rosbags, please wait for a while.
+
 ## 4. Rosbag Example
 ### 4.1 Indoor rosbag (Livox Avia LiDAR)
 
@@ -97,7 +131,9 @@ Download [avia_indoor_quick_shake_example1](https://drive.google.com/file/d/1SWm
 ```
 roslaunch fast_lio mapping_avia.launch
 rosbag play YOUR_DOWNLOADED.bag
+
 ```
+
 ### 4.2 Outdoor rosbag (Livox Avia LiDAR)
 
 <div align="center"><img src="doc/results/HKU_MB_002.png" width=100% /></div>
@@ -106,17 +142,20 @@ rosbag play YOUR_DOWNLOADED.bag
 
 Download [avia_hku_main building_mapping](https://drive.google.com/file/d/1GSb9eLQuwqmgI3VWSB5ApEUhOCFG_Sv5/view?usp=sharing) and then
 ```
-roslaunch fast_lio mapping_avia_outdoor.launch
+roslaunch fast_lio mapping_avia.launch
 rosbag play YOUR_DOWNLOADED.bag
 ```
 
 ## 5.Implementation on UAV
 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.
 
+The main structure of this UAV is 3d printed (Aluminum or PLA), the .stl file will be open-sourced in the future.
+
 <div align="center">
     <img src="doc/uav01.jpg" width=40.5% >
     <img src="doc/uav_system.png" width=57% >
 </div>
 
 ## 6.Acknowledgments
-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).
+
+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), [LINS](https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM) and [Loam_Livox](https://github.com/hku-mars/loam_livox).
diff --git a/config/velody16.yaml → config/velodyne.yaml b/config/velody16.yaml → config/velodyne.yaml
@@ -14,7 +14,7 @@ mapping:
     b_gyr_cov: 0.0001
     fov_degree:    180
     det_range:     100.0
-    extrinsic_T: [ 0, 0, 0.28] #ULHK   #[ -0.5, 1.4, 1.5 ] #utbm
+    extrinsic_T: [ 0, 0, 0.28]
     extrinsic_R: [ 1, 0, 0, 
                    0, 1, 0, 
                    0, 0, 1]
diff --git a/launch/gdb_debug_example.launch b/launch/gdb_debug_example.launch
@@ -2,12 +2,6 @@
 
   <arg name="rviz" default="true" />
 
-  <node pkg="fast_lio" type="loam_feat_extract" name="feature_extract" output="log" />
-  	<param name="lidar_type" type="int" value="1"/>
-  	<param name="feature_extract_enable" type="bool" value="0"/>
-	<param name="blind" type="double" value="1"/>
-	<param name="point_filter_num" type="int" value="3"/>
-
   <node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" required="true" launch-prefix="gdb -ex run --args">
 	<param name="imu_topic" type="string" value="/livox/imu" />
 	<param name="map_file_path" type="string" value=" " />
@@ -17,7 +11,7 @@
 	<param name="filter_size_corner" type="double" value="0.2" />
 	<param name="filter_size_surf" type="double" value="0.2" />
 	<param name="filter_size_map" type="double" value="0.5" />
-	<param name="runtime_pos_log" type="bool" value="1" />
+	<param name="runtime_pos_log_enable" type="bool" value="1" />
 	<param name="cube_side_length" type="double" value="2000" />
   </node>
 

diff --git a/launch/mapping_avia.launch b/launch/mapping_avia.launch
@@ -12,7 +12,7 @@
 	<param name="filter_size_surf" type="double" value="0.5" />
 	<param name="filter_size_map" type="double" value="0.5" />
 	<param name="cube_side_length" type="double" value="1000" />
-	<param name="runtime_pos_log" type="bool" value="0" />
+	<param name="runtime_pos_log_enable" type="bool" value="0" />
     <node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" /> 
 
 	<group if="$(arg rviz)">

diff --git a/launch/mapping_horizon.launch b/launch/mapping_horizon.launch
@@ -12,7 +12,7 @@
 	<param name="filter_size_surf" type="double" value="0.5" />
 	<param name="filter_size_map" type="double" value="0.5" />
 	<param name="cube_side_length" type="double" value="1000" />
-	<param name="runtime_pos_log" type="bool" value="0" />
+	<param name="runtime_pos_log_enable" type="bool" value="0" />
     <node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen"  /> 
 
 	<group if="$(arg rviz)">

diff --git a/launch/mapping_velody16.launch → launch/mapping_velodyne.launch b/launch/mapping_velody16.launch → launch/mapping_velodyne.launch
@@ -3,7 +3,7 @@
 
     <arg name="rviz" default="true" />
 
-    <rosparam command="load" file="$(find fast_lio)/config/velody16.yaml" />
+    <rosparam command="load" file="$(find fast_lio)/config/velodyne.yaml" />
 
     <param name="feature_extract_enable" type="bool" value="0"/>
     <param name="point_filter_num" type="int" value="4"/>
@@ -12,7 +12,7 @@
     <param name="filter_size_surf" type="double" value="0.5" />
     <param name="filter_size_map" type="double" value="0.5" />
     <param name="cube_side_length" type="double" value="1000" />
-    <param name="runtime_pos_log" type="bool" value="0" />
+    <param name="runtime_pos_log_enable" type="bool" value="0" />
     <node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" /> 
 
     <group if="$(arg rviz)">