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A probabilistic voxelmap-based LiDAR-Inertial Odometry.
| Date | Stars |
|---|---|
| 2026-07-24 | 548 |
| 2026-07-25 | 548 |
| 2026-07-28 | 548 |
| 2026-07-30 | 548 |
| 2026-08-06 | 548 |
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# PV-LIO
PV-LIO is a probabilistic voxelmap-based LiDAR-Inertial Odometry. It fuses LiDAR feature points with IMU data using IKFoM to allow robust navigation in fast-motion or narrow environments where degeneration occurs. PV-LIO also supports online LiDAR-IMU extrinsic estimation.
We utilize [VoxelMap](https://github.com/hku-mars/VoxelMap) as the Local Map manager of PV-LIO, it calculates the covariance of each ```<LiDAR point,planar feature>``` correspondence according to the LiDAR ranging model and uses it as confidence ratio to guide the update of KF. This enables robust pose estimation in degenerated scenarios such as narrow staircases. We derive the covariance propagation incorporating the LiDAR-IMU extrinsic parameters, enabling state estimation with IMU and online LiDAR-IMU calibration. We also implement a parallel-optimized map update module, which allows for a more efficient map update than the original implementation of VoxelMap.
### Some test results are shown below:
#### Visualization of voxelmap with uncertainty (Hilti 2022 exp11)
<div align="left">
<img src="doc/voxelmap.jpg" width=95.5% />
</div>
#### Narrow Environment Test
**Left**: Robosense RS16, staircase_crazy_rotation dataset
**Right**: Livox AVIA, long_tunnel dataset
<div align="left">
<img src="doc/stair.gif" width=47.5% /> <img src="doc/tunnel.gif" width=47.5% />
</div>
#### Hilti 2022 exp11
<div align="left">
<img src="doc/hilti11.gif" width=95% />
</div>
#### Hilti 2022 exp15
<div align="left">
<img src="doc/hilti15.gif" width=95% />
</div>
#### Hilti 2022 exp03
<div align="left">
<img src="doc/hilti03.gif" width=95% />
</div>
## Update
- 2023.07.18: Fix eigen failed error for Ubuntu 20.04.
## 1. Prerequisites
### 1.1 **Ubuntu** and **ROS**
**Ubuntu >= 16.04**
For **Ubuntu 18.04 or higher**, the **default** PCL and Eigen is enough for PV-LIO to work normally.
ROS >= Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)
### 1.2. **PCL && Eigen**
PCL >= 1.8, Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).
Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
### 1.3. **livox_ros_driver**
Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
*Remarks:*
- The **livox_ros_driver** must be installed and **sourced** before run any PV-LIO launch file.
- How to source? The easiest way is add the line ``` source $Livox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Livox_ros_driver_dir$ ``` 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 ~/$A_ROS_DIR$/src
git clone https://github.com/hviktortsoi/PV_LIO.git
cd PV_LIO
cd ../..
catkin_make
source devel/setup.bash
```
- 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
Noted:
A. Please make sure the IMU and LiDAR are **Synchronized**, that's important.
B. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important for the forward propagation and backwark propagation.
### 3.1 For Livox 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 ~/$PV_LIO_ROS_DIR$
source devel/setup.bash
roslaunch pv_lio mapping_avia.launch
roslaunch livox_ros_driver livox_lidar_msg.launch
```
- For livox serials, PV-LIO only support the data collected by the ``` livox_lidar_msg.launch ``` since only its ``` livox_ros_driver/CustomMsg ``` data structure produces the timestamp of each LiDAR poExcerpt of 9,577 characters
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Erjan K · Netherlands
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:afcb45e5c7effdc6, topic:slam