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
静心问道9 分钟前
TrOCR: 基于Transformer的光学字符识别方法,使用预训练模型
人工智能·深度学习·transformer·多模态
说私域11 分钟前
基于开源AI大模型、AI智能名片与S2B2C商城小程序源码的用户价值引导与核心用户沉淀策略研究
人工智能·开源
亲持红叶12 分钟前
GLU 变种:ReGLU 、 GEGLU 、 SwiGLU
人工智能·深度学习·神经网络·激活函数
说私域12 分钟前
线上协同办公时代:以开源AI大模型等工具培养网感,拥抱职业变革
人工智能·开源
群联云防护小杜14 分钟前
深度隐匿源IP:高防+群联AI云防护防绕过实战
运维·服务器·前端·网络·人工智能·网络协议·tcp/ip
摘星编程19 分钟前
构建智能客服Agent:从需求分析到生产部署
人工智能·需求分析·智能客服·agent开发·生产部署
不爱学习的YY酱22 分钟前
信息检索革命:Perplexica+cpolar打造你的专属智能搜索中枢
人工智能
whaosoft-1432 小时前
51c自动驾驶~合集7
人工智能
刘晓倩5 小时前
Coze智能体开发实战-多Agent综合实战
人工智能·coze
石迹耿千秋6 小时前
迁移学习--基于torchvision中VGG16模型的实战
人工智能·pytorch·机器学习·迁移学习