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Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections

preview.mp4

This ROS-package provides extrinsic calibration for a static camera network providing person keypoint detections.
We assume the intrinsic calibration and a rough estimate of the extrinsic calibration to be available.

Installation

Dependencies

The package was tested with ROS melodic and Ubuntu 18.04, as well as ROS noetic and Ubuntu 20.04.

The former requires the geometry2 and cv_bridge packages to be placed in the catkin_ws/src folder.
Both packages must be built with Python3 support, e.g. using

catkin_make -DPYTHON_EXECUTABLE:FILEPATH=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so

The received person keypoint detections must be encoded using the person_msgs package with the default joint order being defined here. Place the person_msgs package it in your catkin_ws/src folder and build it via catkin_make or catkin_build person_msgs.

The factor graph optimization is implemented using the GTSAM library,
which can be installed by pip install gtsam==4.1.1.

For additional standard-dependencies, see calibration.py.

Build

Place the keypoint_camera_calibration package in your catkin_ws/src folder.
Navigate to your catkin_ws and run catkin_make or catkin_build keypoint_camera_calibration,
depending on your build system.

Demo

The examples folder contains calibration files for the presented camera network.
The initial estimate of the extrinsic calibration gets generated automatically by retracting the reference calibration.
The corresponding calibration and evaluation bagfiles can be found here.
All parameters are preset for this scenario.

Simply start the calibration pipeline by

rosrun keypoint_camera_calibration calibration.py

Play one of the provided bagfiles to start calibration

rosbag play $(rospack find keypoint_camera_calibration)/examples/bagfiles/2022-05-26_calib_2persons_3min.bag

Results will be placed in the logs folder.
Visualization presets for rqt and rviz are provided.

General Usage

Provide .yaml files following the syntax established in the example files:

  • Intrinsic calibration
  • Estimated extrinsic calibration
  • Reference extrinsic calibration (optional)

Edit the required parameters in the __init__ function of calibration.py to match your scenario:

  • File locations
  • Message properties
  • Method parameters

Start calibration by

rosrun keypoint_camera_calibration calibration.py

Provide person_msgs by playing a bagfile or accessing a sensor network.

Citation

Bastian Pätzold, Simon Bultmann, and Sven Behnke:
Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections.
DAGM German Conference on Pattern Recognition (GCPR), Konstanz, September 2022.

License

This package is licensed under BSD-3.