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SSL_LIO: Solid-State LiDAR based LiDAR-Inertial SLAM

License: GNU General Public License v3.0

CMake 1.29% C++ 77.64% Python 21.06%

mapping ros slam

ssl_slam3's Introduction

SSL_SLAM3

Lightweight 3-D Localization and Mapping for Solid-State LiDAR (Intel Realsense L515 as an example)

This work provides a basic fusion framework that fuses LiDAR and IMU information to improve the stability performance of SSL_SLAM

If you would like to enable save map and test localization separately, you can check this repo: SSL_SLAM2

This code is an improved implementation of paper "Lightweight 3-D Localization and Mapping for Solid-State LiDAR", accepted in IEEE Robotics and Automation Letters, 2021

A summary video demo can be found at Video

Modifier: Wang Han, Nanyang Technological University, Singapore

1. Solid-State Lidar Sensor Example

1.1 Scene reconstruction

1.2 SFM building example

1.3 Localization and Mapping with L515

2. Prerequisites

2.1 Ubuntu and ROS

Ubuntu 64-bit 18.04.

ROS Noetic. ROS Installation

2.2. Ceres Solver

Follow Ceres Installation.

2.3. PCL

Follow PCL Installation.

Tested with 1.8.1

2.4. Trajectory visualization

For visualization purpose, this package uses hector trajectory sever, you may install the package by

sudo apt-get install ros-noetic-hector-trajectory-server

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed

3. Build

3.1 Clone repository:

    cd ~/catkin_ws/src
    git clone https://github.com/wh200720041/ssl_slam3.git
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

3.2 Download test rosbag

You may download our recorded data (7.8GB) if you dont have realsense L515, and by defult the file should be under home/user/Downloads unzip the file

cd ~/Downloads
unzip ~/Downloads/library.zip

3.3 Launch ROS

    roslaunch ssl_slam3 ssl_slam3.launch

4. Sensor Setup

If you have new Realsense L515 sensor, you may follow the below setup instructions

4.1 IMU calibration (optional)

You may read official document [L515 Calibration Manual] (https://github.com/l515_calibration_manual.pdf) first

use the following command to calibrate imu, note that the build-in imu is a low-grade imu, to get better accurate, you may use your own imu

cd ~/catkin_ws/src/ssl_slam3/l515_imu_calibration
python rs-imu-calibration.py

4.2 L515

4.3 Librealsense

Follow Librealsense Installation

4.4 Realsense_ros

Copy realsense_ros package to your catkin folder

    cd ~/catkin_ws/src
    git clone https://github.com/IntelRealSense/realsense-ros.git
    cd ..
    catkin_make

4.5 Launch ROS

Make Lidar still for 1 sec to estimate the initial bias, otherwise will cause localization failure!

    roslaunch ssl_slam3 ssl_slam3_L515.launch

5. Citation

If you use this work for your research, you may want to cite the paper below, your citation will be appreciated

@article{wang2021lightweight,
  author={H. {Wang} and C. {Wang} and L. {Xie}},
  journal={IEEE Robotics and Automation Letters}, 
  title={Lightweight 3-D Localization and Mapping for Solid-State LiDAR}, 
  year={2021},
  volume={6},
  number={2},
  pages={1801-1807},
  doi={10.1109/LRA.2021.3060392}}

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ssl_slam3's Issues

not enough correct surf points

Hello @wh200720041,

Thank you for the great work.

I setup the repo on my PC (ubuntu 20.04, with noetic) and tried the ssl_slam3.launch with the standard bag file and it worked fine. When I launch the ssl_slam3_L515.launch for the live run though, I immediately get the messages "not enough correct surf points" and "not enough correct edge points" and the map spirals out. I tried with all frequencies and resolutions (for RGB and depth) and the issue is still the same.

Hoping you could assist with the same.

rslidar M1

I have a solid lidar called rslidar M1,could this code map successful on rslidar M1?

Derivation of jacobian formula

Hi, @wh200720041

Thanks for your great work.

I have a question about the derivation of jacobian formula in imuOptimizationFactor.cpp.

The residual function is:
`

// residual 1 = log[delta_r' * Ri' * Rj]
// residual 2 = Ri'* (0.5 * g *dt2 + Pj - Pi -Vi * dt) - delta_p
// residual 3 = Ri'* (g * dt + Vj - Vi) - delta_v
// residual 4 = b_aj - b_ai
// residual 5 = b_gj - b_gi

the optimized variable is r p v ba bg

I'm confused about how do you derive the formula
`

       jacobian_i.block<3, 3>(0, 0) = - Utils::Jr_so3_inv(rot_residual) * Rj.transpose() * Ri;
        jacobian_i.block<3, 3>(0, 12) = - Utils::Jr_so3_inv(rot_residual) * Rj.transpose() * Ri * corrected_delta_R * dr_dbg;

and jacobian_j.block<3, 3>(0, 0) = Utils::Jr_so3_inv(rot_residual);

Can you give me some help?

IMU bias

Hi, @wh200720041

I have test the code for a modified version of traditional spinning lidar.
The only modification is feature extraction, the moudle is implemented based on lio-sam.
After the adjustment, everything is ok ,and it seems normal with my data.

I added a distortion correction module, use the imu preintegrate predict pose. But it seems something wrong, distortion correction did not work well.
I debug the question,and find out that the imu bias is small. It is not normal.

The possible reason maybe：

the adjustment is not right.
something wrong with the the imu preintegrate.

Can you give me some advice?
Thanks.

Derivation of jacobian of LidarEdgeFactor

hello, @wh200720041

Thanks for your great work.

I have a question about the derivation of jacobian formula in lidarOptimizationFactor.cpp

In factor LidarEdgeFactor:
Eigen::Matrix<double, 3, 15> dp_by_so3xyz = Eigen::Matrix<double, 3, 15>::Zero();
dp_by_so3xyz.block<3, 3>(0, 0) = - Ri * Utils::skew(curr_point);
dp_by_so3xyz.block<3, 3>(0, 3) = Eigen::Matrix3d::Identity();

I'm confused about why is a Ri here? how do you derive the formula?

Can you give me some help ?

Thanks

why "con.wait(lk, [&]{return synchronizeSensors();}); " last fo song long time.

I tested it, it lasted for about 40ms. Dose this part really need so much time?
And does your research group accept students from CSC exchange? I want to come and study, your work really impressed me.
Hope for your reply.

wh200720041 / ssl_slam3 Goto Github PK

ssl_slam3's Introduction

SSL_SLAM3

Lightweight 3-D Localization and Mapping for Solid-State LiDAR (Intel Realsense L515 as an example)

This work provides a basic fusion framework that fuses LiDAR and IMU information to improve the stability performance of SSL_SLAM

1. Solid-State Lidar Sensor Example

1.1 Scene reconstruction

1.2 SFM building example

1.3 Localization and Mapping with L515

2. Prerequisites

2.1 Ubuntu and ROS

2.2. Ceres Solver

2.3. PCL

2.4. Trajectory visualization

3. Build

3.1 Clone repository:

3.2 Download test rosbag

3.3 Launch ROS

4. Sensor Setup

4.1 IMU calibration (optional)

4.2 L515

4.3 Librealsense

4.4 Realsense_ros

4.5 Launch ROS

5. Citation

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