unsigned-long / river

[IEEE RA-L 2024] River: A Tightly-Coupled Radar-Inertial Velocity Estimator Based on Continuous-Time Optimization

River: A Tightly-coupled Radar-inertial Velocity Estimator

0. Preliminaries

If you use River in a scientific publication, please cite the following paper 😄:

[IEEE RA-L 2024] River: A Tightly-coupled Radar-inertial Velocity Estimator Based on Continuous-time Optimization

@ARTICLE{10529532,
  author={Chen, Shuolong and Li, Xingxing and Li, Shengyu and Zhou, Yuxuan and Wang, Shiwen},
  journal={IEEE Robotics and Automation Letters}, 
  title={River: A Tightly-coupled Radar-inertial Velocity Estimator Based on Continuous-time Optimization}, 
  year={2024},
  volume={},
  number={},
  pages={1-8},
  keywords={Estimation;Radar;Splines (mathematics);Radar measurements;Optimization;Velocity measurement;Sensors;Ego-velocity estimation;millimeter-wavelength radar;inertial measurement unit;continuous-time optimization},
  doi={10.1109/LRA.2024.3400154}}

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News

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Considering the lack of an open-source radar-inertial spatiotemporal calibrator currently, we also developed a targetless spatiotemporal calibrator for multiple radars and IMUs, termed as RIs-Calib, which

• supports one-shot multi-radar multiIMU spatial and temporal determination;
• not require any additional artificial infrastructure or prior knowledge;
• capable of accurate and consistent calibration;

The GitHub repository of RIs-Calib now is available at https://github.com/Unsigned-Long/RIs-Calib.git, which contains all source code and mathematics documentation in implementations. Demo video of RIs-Calib is also available.

1. Overview

Continuous and reliable ego-velocity information is significant for high-performance motion control and planning in a variety of robotic tasks, such as autonomous navigation and exploration. While linear velocities as first-order kinematics can be simultaneously estimated with other states or explicitly obtained by differentiation from positions in ego-motion estimators such as odometers, the high coupling leads to instability and even failures when estimators degenerate. To this end, we present River: an accurate and continuous linear velocity estimator that efficiently fuses high-frequency inertial and radar target measurements based on continuous-time optimization. Specifically, a dynamic initialization procedure is first performed to rigorously recover the initials of states, followed by batch estimations, where the velocity and rotation B-splines would be optimized incrementally to provide continuous body-frame velocity estimates. Results from both simulated and real-world experiments demonstrate that River is capable of high accuracy, repeatability, and consistency for ego-velocity estimation. We open-source our implementations here to benefit the research community.

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Demo Video for River

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Our accompanying videos are now available on YouTube (click below images to open) and Bilibili.

2. Build River

2.1 Preparation

• install ROS1 (Ubuntu 20.04 is suggested, Ubuntu 18.04 (ros melodic) is also available):

  sudo apt install ros-noetic-desktop-full
  echo "source /opt/ros/noetic/setup.bash" >> ~/.bashrc
  source ~/.bashrc

  Requirements: ROS1 & C++17 Support

• install Ceres:

  see the GitHub Profile of Ceres library, clone it, compile it, and install it. Make sure that the version of Ceres contains the Manifold module. (Ceres version equal to 2.2.0 or higher than that)

• install Sophus:

  see the GitHub Profile of Sophus library, clone it, compile it, and install it. Set optionSOPHUS_USE_BASIC_LOGGING ON when compile (cmake) the Sophus library, this would avoid to involve fmt logger dependency (as the following spdlog would use internal fmt too, which may lead to conflict).

• install magic-enum:

  see the GitHub Profile of magic-enum library, clone it, compile it, and install it.

• install Pangolin:

  see the GitHub Profile of Pangolin library, clone it, compile it, and install it.

• install spdlog:

  see the GitHub Profile of spdlog library, clone it, compile it, and install it. Though this library can be installed by sudo apt-get install libspdlog-dev, the version is too old to support river, thus installing from the source is recommended.

• install cereal, yaml-cpp:

  sudo apt-get install libcereal-dev
  sudo apt-get install libyaml-cpp-dev

2.2 Clone and Compile River

• create a ros workspace if needed and clone River to src directory as river:

  mkdir -p ~/River/src
  cd ~/River/src
  
  git clone --recursive https://github.com/Unsigned-Long/River.git river

  change directory to 'river', and run 'build_thirdparty.sh'.

  cd river
  chmod +x build_thirdparty.sh
  ./build_thirdparty.sh

  this would build 'tiny-viewer' and 'ctraj' libraries.

• Prepare for thirdparty ros packages:

  clone ros packages 'ainstein_radar', 'ti_mmwave_rospkg', 'serial', 'sbg_ros_driver' to 'river/..' (directory at the same level as river):

  cd ..
  git clone https://github.com/AinsteinAI/ainstein_radar.git
  git clone https://github.com/Unsigned-Long/ti_mmwave_rospkg.git
  git clone https://github.com/wjwwood/serial.git
  git clone https://github.com/SBG-Systems/sbg_ros_driver.git

  then change directory to the ros workspace to build these packages:

  cd ..
  catkin_make -DCATKIN_WHITELIST_PACKAGES="ainstein_radar;ti_mmwave_rospkg;serial;sbg_driver"

  Note that these packages will depend on many other ros packages, you need to install them patiently.

• compile River:

  catkin_make -DCATKIN_WHITELIST_PACKAGES=""

3. Launch River

Attention: to create a virtual reality (VR) perspective of the IMU (left window view in runtime), you have to change the model file path in configure field (Preference::ObjFileForDisplay) to {root path}/river/model/river.obj. For a better VR perspective, you can design your own simulation scenario using Blender and export it as an obj file, then pass it to the configure file.

3.1 Simulation Test

simulation experiments (datasets, launch, result visualization)

3.2 Real-world Experiments

real-world experiments (datasets, launch, result visualization)

3.3 Skip Tutorial

Find a configure file in river/dataset, then change the fields in the configure files to be compatible with your dataset (there are detailed comments for each field). You only need to change a few fields related to io (input and output), perhaps some additional fields related to optimization.

Then give the path of your configuration file to the launch file of river in folder river/launch (handheld, xrio, or simu-test folder), Then, we launch 'river':

roslaunch river {the-launch-filename}.launch

The corresponding results would be output to the directory you set in the configure file when solving finished (or interrupted).

river's Issues

double free or corruption (out)

“Bro, I successfully compiled RIVER and RIS-CALIB in Docker, but encountered the same issue for both: 'double free or corruption (out)'. Hope you can provide some guidance!”

能否创建一个DOCKER 容器

在编译ctrij的时候，ceres2.2.0的版本无法安装，一直报错
如果有一个docker 就完美了