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basalt/doc/Calibration.md

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Calibration

Here, we explain how to use the calibration tools with TUM-VI and EuRoC datasets as an example.

TUM-VI dataset

Download the datasets for camera and camera-IMU calibration:

mkdir ~/tumvi_calib_data
cd ~/tumvi_calib_data
wget http://vision.in.tum.de/tumvi/calibrated/512_16/dataset-calib-cam3_512_16.bag
wget http://vision.in.tum.de/tumvi/calibrated/512_16/dataset-calib-imu1_512_16.bag

Camera calibration

Run the camera calibration:

basalt_calibrate --dataset-path ~/tumvi_calib_data/dataset-calib-cam3_512_16.bag --dataset-type bag --result-path ~/tumvi_calib_result/ --cam-types ds ds

The command line options have the following meaning:

  • --dataset-path path to the dataset.
  • --dataset-type type of the datset. Currently only bag and euroc formats of the datasets are supported.
  • --result-path path to the folder where the resulting calibration and intermediate results will be stored.
  • --cam-types camera models for the image streams in the dataset. For more detais see arXiv:1807.08957.

After that, you should see the calibration GUI: tumvi_cam_calib

The buttons in the GUI are located in the order which you should follow to calibrate the camera. After pressing a button the system will print the output to the command line:

  • load_dataset loads the dataset.
  • detect_corners starts corner detection in the backround thread. Since it is the most time consuming part of the calibration process, the detected corners are cached and loaded if you run the executable again pointing to the same result folder path.
  • init_cam_intr computes an initial guess for camera intrinsics.
  • init_cam_poses computes an initial guess for camera poses given the current intrinsics.
  • init_cam_extr computes an initial transformation between the cameras.
  • init_opt initializes optimization and shows the projected points given the current calibration and camera poses.
  • optimize runs an iteration of the optimization and visualizes the result. You should press this button until the error printed in the console output stops decreasing and the optimization converges.
  • save_calib saves the current calibration as calibration.json in the result folder.
  • compute_vign (Experimental) computes a radially-symmetric vignetting for the cameras. For the algorithm to work, the calibration pattern should be static (and camera moving around it) and have a constant lighting throughout the calibration sequence. If you run compute_vign you should press save_calib afterwards. The png images with vignetting will also be stored in the result folder.

You can also control the process using the following buttons:

  • show_frame slider to switch between the frames in the sequence.
  • show_corners toggles the visibility of the detected corners shown in red.
  • show_corners_rejected toggles the visibility of rejected corners.
  • show_init_reproj shows the initial reprojections computed by the init_cam_poses step.
  • show_opt shows reprojected corners with the current estimate of the intrinsics and poses.
  • show_vign toggles the visibility of the points used for vignetting estimation. The points are distributed across white areas of the pattern.
  • show_ids toggles the ID visualization for every point.
  • huber_thresh controls the threshold for the huber norm in pixels for the optimization.
  • opt_intr controls if the optimization can change the intrinsics. For some datasets it might be helpful to disable this option for several first iterations of the optimization.

Camera + IMU + Mocap calibration

After calibrating the camera you can run the camera + IMU + Mocap calibration. The result path should point to the same folder as before:

basalt_calibrate_imu --dataset-path ~/tumvi_calib_data/dataset-calib-imu1_512_16.bag --dataset-type bag --result-path ~/tumvi_calib_result/ --gyro-noise-std 0.000282 --accel-noise-std 0.016 --gyro-bias-std 0.0001 --accel-bias-std 0.001

The command line options for the IMU noise are continous-time and defined as in Kalibr:

  • --gyro-noise-std gyroscope white noise.
  • --accel-noise-std accelerometer white noise.
  • --gyro-bias-std gyroscope random walk.
  • --accel-bias-std accelerometer random walk.

tumvi_imu_calib

The buttons in the GUI are located in the order which you need to follow to calibrate the camera-IMU setup:

  • load_dataset, detect_corners, init_cam_poses same as above.
  • init_cam_imu initializes the rotation between camera and IMU by aligning rotation velocities of the camera to the gyro data.
  • init_opt initializes the optimization. Shows reprojected corners in magenta and the estimated values from the spline as solid lines below.
  • optimize runs an iteration of the optimization. You should press it several times until convergence before proceeding to next steps.
  • init_mocap initializes the transformation from the Aprilgrid calibration pattern to the Mocap coordinate system. Currently we assume that the orientation of the Mocap marker frame is approximately aligned with IMU axes.
  • save_calib save the current calibration as calibration.json in the result folder.
  • save_mocap_calib save the current Mocap to IMU calibration as mocap_calibration.json in the result folder.

You can also control the visualization using the following buttons:

  • show_frame - show_ids the same as above.
  • show_spline toggles the visibility of enabled measurements (accel, gyro, position, velocity) generated from the spline that we optimize.
  • show_data toggles the visibility of raw data containted in the dataset.
  • show_accel shows accelerometer data.
  • show_gyro shows gyroscope data.
  • show_pos shows the position data.
  • show_mocap shows the mocap marker position transformed to the IMU frame.
  • show_mocap_rot_error shows rotation between the spline and Mocap measurements.
  • show_mocap_rot_vel shows the rotation velocity computed from the Mocap.

The following options control the optimization process:

  • opt_intr enables optimization of intrinsics. Usually should be disabled for the camera-IMU calibration.
  • opt_poses enables optimization based camera pose initialization. Sometimes helps to better initialize the spline before running optimization with opt_corners.
  • opt_corners enables optimization based on reprojection corner positions (should be used by default).
  • opt_cam_time_offset computes the time offset between camera and the IMU. This option should be used only for refinement when the optimization already converged.
  • opt_imu_scale enables IMU axis scaling, rotation and mislignment calibration. This option should be used only for refinement when the optimization already converged.
  • opt_mocap enables Mocap optimization. You should run it only after pressing init_mocap.
  • huber_thresh controls the threshold for the huber norm in pixels for the optimization.

NOTE: In this case the we use pre-calibrated sequence, so most of refinements or Mocap to IMU calibration will not have any visible effect. If you want to test this functionality use the "raw" sequences, for example http://vision.in.tum.de/tumvi/raw/dataset-calib-cam3.bag and http://vision.in.tum.de/tumvi/raw/dataset-calib-imu1.bag.

EuRoC dataset

Download the datasets for camera and camera-IMU calibration:

mkdir ~/euroc_calib_data
cd ~/euroc_calib_data
wget http://robotics.ethz.ch/~asl-datasets/ijrr_euroc_mav_dataset/calibration_datasets/cam_april/cam_april.bag
wget http://robotics.ethz.ch/~asl-datasets/ijrr_euroc_mav_dataset/calibration_datasets/imu_april/imu_april.bag

Camera calibration

Run the camera calibration:

basalt_calibrate --dataset-path ~/euroc_calib_data/cam_april.bag --dataset-type bag --result-path ~/euroc_calib_result/ --cam-types ds ds

euroc_cam_calib

Camera + IMU calibration

After calibrating the camera you can run the camera + IMU calibration. The result-path should point to the same folder as before:

basalt_calibrate_imu --dataset-path ~/euroc_calib_data/imu_april.bag --dataset-type bag --result-path ~/euroc_calib_result/ --gyro-noise-std 0.000282 --accel-noise-std 0.016 --gyro-bias-std 0.0001 --accel-bias-std 0.001

euroc_imu_calib