Multi - LiCa 多激光雷达标定

https://github.com/TUMFTM/Multi_LiCa

Multi - LiCa

Multi - LiDAR-to-LiDAR calibration framework for ROS 2 and non-ROS applications




Introduction

This project provides an extrinsic calibration framework for quickly calibrating multiple LiDAR sensors. It employs the Generalized Iterative Closest Point (GICP) algorithm for LiDAR-to-LiDAR extrinsic calibration and uses the RANdom SAmple Consensus (RANSAC) method to calibrate the pitch and z-distance to the ground of a single LiDAR, assuming other coordinates are known.

It has proven to be robust for different sensor setups and environments without the need of an initial guess.

We use a FPFH-based feature vector creation with an TEASER++ feature matching for the coarse alignment, which is used as initial guess for the GICP algorithm.

Overview

Motion- and targetless multi - LiDAR-to-LiDAR Calibration Pipeline,
developed at the Institute of Automotive Technology, TUM

Limitations

  • Our tool was specifically developed for motionless calibration.
  • We assume that each LiDAR to be calibrated has either a directly overlapping FOV with the target LiDAR FOV or has overlap with other LiDAR(s) with overlap to the target. This can be cascading dependency to the target.
  • We assume that the ground is flat and the environment is static.
  • Input point clouds for the calibration are in sensor_msgs/PointCloud2 or in .pcd format.

Prerequisites

The bare minimum requirement for our tool is a Linux-based OS and Docker, as we provide a Docker image with our framework. You do not need to build anything locally, but you are free to do so as described in the following section. For the local build, you will need ROS 2 - humble, Python 3.10 with opend3d, scipy, ros2_numpy and pandas (optional).

Installation and Usage

🐋 Docker Environment

  1. Build the Docker image:

    复制代码
    ./docker/build_docker.sh
  2. Run the container:

    复制代码
    ./docker/run_docker.sh

🖥 Local Build

  1. Install ROS2 humble (might work with other ROS2 distributions but wasn't tested):
    https://docs.ros.org/en/humble/Installation.html

  2. Create a ROS 2 workspace:

    复制代码
    mkdir -p ~/ros2_ws
    cd ~/ros2_ws
  3. Clone the repository:

    复制代码
    git clone git@github.com:TUMFTM/Multi_LiCa.git
  4. Install dependencies:

    复制代码
    cd Multi_LiCa
    pip install --no-cache-dir --upgrade pip
    pip install --no-cache-dir -r requirements.txt
  5. Source the ROS 2 environment and build the project using colcon:

    复制代码
    source /opt/ros/$ROS_DISTRO/setup.bash
    colcon build --symlink-install --packages-up-to multi_lidar_calibrator --cmake-args -DCMAKE_BUILD_TYPE=Release

⏯️ Usage

  1. Configure the parameters to fit your data:

    复制代码
    vim config/<params-file>.yaml
  2. Launch the multi_lidar_calibrator node:

    复制代码
    ros2 launch multi_lidar_calibrator calibration.launch.py parameter_file:=/path/to/parameter/file

⚙️ Configuration

  • We provided a detailed parameter file with explanation with config/params.yaml

  • Configure config/params.yaml to fit your data. Depending on the application, you may need to specify the LiDARs, paths to .pcd files, or LiDAR topic names. You may also change GICP and RANSAC parameters.

  • In addition to LiDAR-to-LiDAR calibration, you can perform target LiDAR-to-ground/base calibration if your x,y translation and roll, yaw rotation are precisely known.

    If you are using to-base calibration, you may choose a URDF file to save the calibration so that it can be directly used in your ROS robot-state-publisher.

  • When running in a container, ensure that your local and container environments have the same ROS_DOMAIN_ID. If not, set it to be the same with export ROS_DOMAIN_ID=<ID>.

  • When using ROS 2, verify that the transformation guess is published on the /tf_static topic and that the data is published for all specified LiDARs.

🎞️ Demo

On default, the tool will launch a demo with data from OpenCalib.

It will open a window and will display three pointclouds and their initial transforms. You can inspect the files in the interactive window. After closing the window (press Q), the tool will calculate the transformations ans will print the results to the terminal, write them to the output directory and will display a windows with the transformed pointclouds.

Other OSS Calibration Frameworks

相关推荐
坤坤爱学习2.03 分钟前
求医十年,病因不明,ChatGPT:你看起来有基因突变
人工智能·ai·chatgpt·程序员·大模型·ai编程·大模型学
蹦蹦跳跳真可爱58938 分钟前
Python----循环神经网络(Transformer ----注意力机制)
人工智能·深度学习·nlp·transformer·循环神经网络
空中湖3 小时前
tensorflow武林志第二卷第九章:玄功九转
人工智能·python·tensorflow
lishaoan773 小时前
使用tensorflow的线性回归的例子(七)
人工智能·tensorflow·线性回归
千宇宙航6 小时前
闲庭信步使用SV搭建图像测试平台:第三十一课——基于神经网络的手写数字识别
图像处理·人工智能·深度学习·神经网络·计算机视觉·fpga开发
onceco6 小时前
领域LLM九讲——第5讲 为什么选择OpenManus而不是QwenAgent(附LLM免费api邀请码)
人工智能·python·深度学习·语言模型·自然语言处理·自动化
jndingxin9 小时前
OpenCV CUDA模块设备层-----高效地计算两个 uint 类型值的带权重平均值
人工智能·opencv·计算机视觉
Sweet锦9 小时前
零基础保姆级本地化部署文心大模型4.5开源系列
人工智能·语言模型·文心一言
hie9889410 小时前
MATLAB锂离子电池伪二维(P2D)模型实现
人工智能·算法·matlab
晨同学032710 小时前
opencv的颜色通道问题 & rgb & bgr
人工智能·opencv·计算机视觉