velo2cam_gazebo repository

Repository Summary

Checkout URI https://github.com/beltransen/velo2cam_gazebo.git
VCS Type git
VCS Version master
Last Updated 2021-01-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
velodyne_gazebo_plugin 1.0.0
velo2cam_gazebo 1.0.0

README

velo2cam_gazebo

Repository including Gazebo models, plugins and worlds to test algorithms for extrinsic calibration of lidar-camera pairs. Package developed at Intelligent Systems Laboratory, Universidad Carlos III de Madrid.

gazebo screenshot

Gazebo models

This repository includes several sensors and calibration target models to evaluate the performance of extrinsic calibration of lidar-camera pair in the Gazebo Simulator. The models need to be moved to ~/.gazebo/models/ if you want access them from Gazebo.

Note: The models included in this repository were designed for evaluating the LIDAR-camera calibration algorithms described in [1] and [2], whose code is provided here.

Sensors:

  • Monocular cameras: FLIR Blackfly S 31S4C-C
  • Stero cameras: FLIR Bumblebee XB3 Camera (Left - center only)
  • LiDARs: Velodyne VLP-16, Velodyne HDL-32, Velodyne HDL-64

Calibration targets:

Gazebo plugins

  • Velodyne plugin providing PointCloud2 with same structure as driver (x, y, z, intensity, ring) and simulated Gaussian noise. (Code from DataspeedInc, although minor patch for vertical resolution issue is included)

Known Issues

  • Gazebo can take up to 30 seconds to load the VLP-16 pluggin, 60 seconds for the HDL-32E, and much more HDL-64E
  • Gazebo cannot maintain 10Hz with large pointclouds
    • Solution: User can reduce number of points in urdf

Gazebo Calibration Scenarios

  • Launch files for all calibrations scenarios used in the related published manuscripts are included. To launch a sample scenario, run:

roslaunch velo2cam_gazebo mono_hdl64_p1_real.launch

Citation

If you use this work in your research, please consider citing the following papers:

[1] Beltrán, J., Guindel, C., and García, F. (2021). Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. arXiv:2101.04431 [cs.RO]. Submitted to IEEE Transactions on Intelligent Transportation Systems. [Preferred citation]

[2] Guindel, C., Beltrán, J., Martín, D., and García, F. (2017). Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups. IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679. Pre-print available here.

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/beltransen/velo2cam_gazebo.git
VCS Type git
VCS Version master
Last Updated 2021-01-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
velodyne_gazebo_plugin 1.0.0
velo2cam_gazebo 1.0.0

README

velo2cam_gazebo

Repository including Gazebo models, plugins and worlds to test algorithms for extrinsic calibration of lidar-camera pairs. Package developed at Intelligent Systems Laboratory, Universidad Carlos III de Madrid.

gazebo screenshot

Gazebo models

This repository includes several sensors and calibration target models to evaluate the performance of extrinsic calibration of lidar-camera pair in the Gazebo Simulator. The models need to be moved to ~/.gazebo/models/ if you want access them from Gazebo.

Note: The models included in this repository were designed for evaluating the LIDAR-camera calibration algorithms described in [1] and [2], whose code is provided here.

Sensors:

  • Monocular cameras: FLIR Blackfly S 31S4C-C
  • Stero cameras: FLIR Bumblebee XB3 Camera (Left - center only)
  • LiDARs: Velodyne VLP-16, Velodyne HDL-32, Velodyne HDL-64

Calibration targets:

Gazebo plugins

  • Velodyne plugin providing PointCloud2 with same structure as driver (x, y, z, intensity, ring) and simulated Gaussian noise. (Code from DataspeedInc, although minor patch for vertical resolution issue is included)

Known Issues

  • Gazebo can take up to 30 seconds to load the VLP-16 pluggin, 60 seconds for the HDL-32E, and much more HDL-64E
  • Gazebo cannot maintain 10Hz with large pointclouds
    • Solution: User can reduce number of points in urdf

Gazebo Calibration Scenarios

  • Launch files for all calibrations scenarios used in the related published manuscripts are included. To launch a sample scenario, run:

roslaunch velo2cam_gazebo mono_hdl64_p1_real.launch

Citation

If you use this work in your research, please consider citing the following papers:

[1] Beltrán, J., Guindel, C., and García, F. (2021). Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. arXiv:2101.04431 [cs.RO]. Submitted to IEEE Transactions on Intelligent Transportation Systems. [Preferred citation]

[2] Guindel, C., Beltrán, J., Martín, D., and García, F. (2017). Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups. IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679. Pre-print available here.

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/beltransen/velo2cam_gazebo.git
VCS Type git
VCS Version master
Last Updated 2021-01-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
velodyne_gazebo_plugin 1.0.0
velo2cam_gazebo 1.0.0

README

velo2cam_gazebo

Repository including Gazebo models, plugins and worlds to test algorithms for extrinsic calibration of lidar-camera pairs. Package developed at Intelligent Systems Laboratory, Universidad Carlos III de Madrid.

gazebo screenshot

Gazebo models

This repository includes several sensors and calibration target models to evaluate the performance of extrinsic calibration of lidar-camera pair in the Gazebo Simulator. The models need to be moved to ~/.gazebo/models/ if you want access them from Gazebo.

Note: The models included in this repository were designed for evaluating the LIDAR-camera calibration algorithms described in [1] and [2], whose code is provided here.

Sensors:

  • Monocular cameras: FLIR Blackfly S 31S4C-C
  • Stero cameras: FLIR Bumblebee XB3 Camera (Left - center only)
  • LiDARs: Velodyne VLP-16, Velodyne HDL-32, Velodyne HDL-64

Calibration targets:

Gazebo plugins

  • Velodyne plugin providing PointCloud2 with same structure as driver (x, y, z, intensity, ring) and simulated Gaussian noise. (Code from DataspeedInc, although minor patch for vertical resolution issue is included)

Known Issues

  • Gazebo can take up to 30 seconds to load the VLP-16 pluggin, 60 seconds for the HDL-32E, and much more HDL-64E
  • Gazebo cannot maintain 10Hz with large pointclouds
    • Solution: User can reduce number of points in urdf

Gazebo Calibration Scenarios

  • Launch files for all calibrations scenarios used in the related published manuscripts are included. To launch a sample scenario, run:

roslaunch velo2cam_gazebo mono_hdl64_p1_real.launch

Citation

If you use this work in your research, please consider citing the following papers:

[1] Beltrán, J., Guindel, C., and García, F. (2021). Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. arXiv:2101.04431 [cs.RO]. Submitted to IEEE Transactions on Intelligent Transportation Systems. [Preferred citation]

[2] Guindel, C., Beltrán, J., Martín, D., and García, F. (2017). Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups. IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679. Pre-print available here.

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/beltransen/velo2cam_gazebo.git
VCS Type git
VCS Version master
Last Updated 2021-01-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
velodyne_gazebo_plugin 1.0.0
velo2cam_gazebo 1.0.0

README

velo2cam_gazebo

Repository including Gazebo models, plugins and worlds to test algorithms for extrinsic calibration of lidar-camera pairs. Package developed at Intelligent Systems Laboratory, Universidad Carlos III de Madrid.

gazebo screenshot

Gazebo models

This repository includes several sensors and calibration target models to evaluate the performance of extrinsic calibration of lidar-camera pair in the Gazebo Simulator. The models need to be moved to ~/.gazebo/models/ if you want access them from Gazebo.

Note: The models included in this repository were designed for evaluating the LIDAR-camera calibration algorithms described in [1] and [2], whose code is provided here.

Sensors:

  • Monocular cameras: FLIR Blackfly S 31S4C-C
  • Stero cameras: FLIR Bumblebee XB3 Camera (Left - center only)
  • LiDARs: Velodyne VLP-16, Velodyne HDL-32, Velodyne HDL-64

Calibration targets:

Gazebo plugins

  • Velodyne plugin providing PointCloud2 with same structure as driver (x, y, z, intensity, ring) and simulated Gaussian noise. (Code from DataspeedInc, although minor patch for vertical resolution issue is included)

Known Issues

  • Gazebo can take up to 30 seconds to load the VLP-16 pluggin, 60 seconds for the HDL-32E, and much more HDL-64E
  • Gazebo cannot maintain 10Hz with large pointclouds
    • Solution: User can reduce number of points in urdf

Gazebo Calibration Scenarios

  • Launch files for all calibrations scenarios used in the related published manuscripts are included. To launch a sample scenario, run:

roslaunch velo2cam_gazebo mono_hdl64_p1_real.launch

Citation

If you use this work in your research, please consider citing the following papers:

[1] Beltrán, J., Guindel, C., and García, F. (2021). Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. arXiv:2101.04431 [cs.RO]. Submitted to IEEE Transactions on Intelligent Transportation Systems. [Preferred citation]

[2] Guindel, C., Beltrán, J., Martín, D., and García, F. (2017). Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups. IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679. Pre-print available here.

CONTRIBUTING

No CONTRIBUTING.md found.