RS-FAST-LIVO

中文介绍

1. Introduction

This repository adapts the state-of-the-art LiDAR-Inertial-Visual odometry system, FAST-LIVO, to our lidar product Active Camera.

FAST-LIVO is a fast LiDAR-Inertial-Visual odometry system, which builds on two tightly-coupled and direct odometry subsystems: a VIO subsystem and a LIO subsystem. The LIO subsystem registers raw points (instead of feature points on e.g., edges or planes) of a new scan to an incrementally-built point cloud map. The map points are additionally attached with image patches, which are then used in the VIO subsystem to align a new image by minimizing the direct photometric errors without extracting any visual features (e.g., ORB or FAST corner features).

If you need further information about the FAST-LIVO algorithm, you can refer to their official website and contributors:

• Website: https://github.com/hku-mars/FAST-LIVO
• Contributors: Chunran Zheng 郑纯然， Qingyan Zhu 朱清岩， Wei Xu 徐威

2. Demos

2.1 Using Robosense Active Camera

"HIT SZ Wall" sequence. left: raw image, right: mapping result

"HIT SZ Wall" sequence. full mapping result

"indoor robosense logo" sequence. left: raw image, right: full mapping result

3. Prerequisited

3.1 ROS2

Follow the specified content in the official tutorial for your operating system.

3.2 Sophus

Sophus Installation for the non-templated/double-only version.

git clone https://github.com/strasdat/Sophus.git
cd Sophus
git checkout a621ff
mkdir build && cd build && cmake ..
make
sudo make install

4. Install and Build

Clone this repository into a create an existing ros2 workspace and execute the following command to build and install:

colcon build --symlink-install 

4. Run the package

4.1 Important parameters

Edit config/RS_META.yaml to set the below parameters:

4.1.1 Algorithm

• lid_topic: The topic name of LiDAR data.
• imu_topic: The topic name of IMU data.
• img_topic: The topic name of camera data.
• img_enable: Enbale vio submodule.
• lidar_enable: Enbale lio submodule.
• point_filter_num: The sampling interval for a new scan. It is recommended that 3~4 for faster odometry, and 1~2 for denser map.
• outlier_threshold: The outlier threshold value of photometric error (square) of a single pixel. It is recommended that 50~250 for the darker scenes, and 500~1000 for the brighter scenes. The smaller the value is, the faster the vio submodule is, but the weaker the anti-degradation ability is.
• img_point_cov: The covariance of photometric errors per pixel.
• laser_point_cov: The covariance of point-to-plane redisual per point.
• filter_size_surf: Downsample the points in a new scan. It is recommended that 0.05~0.15 for indoor scenes, 0.3~0.5 for outdoor scenes.
• filter_size_map: Downsample the points in LiDAR global map. It is recommended that 0.15~0.3 for indoor scenes, 0.4~0.5 for outdoor scenes.
• pcd_save_en: If true, save point clouds to the PCD folder. Save RGB-colored points if img_enable is 1, intensity-colored points if img_enable is 0.
• delta_time: The time offset between the camera and LiDAR, which is used to correct timestamp misalignment.

After setting the appropriate topic name and parameters, you can directly run RS-FAST-LIVO on the dataset.

4.1.2 extrinsic and intrinsic

• extrinsic_T: translation of LiDAR with respect to IMU
• extrinsic_R:: rotation of LiDAR with respect to IMU
• Rcl: translation of LiDAR with respect to camera
• Pcl: rotation of LiDAR with respect to camera
• camera_pinhole_rs.yaml: camera intrinsic

4.2 Run on dataset

Download our collected rosbag files via OneDrive (FAST-LIVO-Datasets) containing xx rosbag files.

ros2 run slam slam_node

5. Acknowledgments

Thanks for FAST-LIVO, FAST-LIVO2 and VINS-Mono

6. License

The source code of this package is released under GPLv2 license.