Here, we explain how to use the calibration tools with TUM-VI, EuRoC, UZH-FPV and Kalibr datasets as an example.
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
Run the camera calibration:
granite_calibrate --dataset-path ~/tumvi_calib_data/dataset-calib-cam3_512_16.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --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 onlybag
andeuroc
formats of the datasets are supported.--result-path
path to the folder where the resulting calibration and intermediate results will be stored.--aprilgrid
path to the configuration file for the aprilgrid.--cam-types
camera models for the image streams in the dataset. For more details see arXiv:1807.08957.
After that, you should see the calibration GUI:
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 background 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. Alternatively, you can use theopt_until_converge
checkbox that will run the optimization until it converges automatically.save_calib
saves the current calibration ascalibration.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 (camera moving around it) and have a constant lighting throughout the calibration sequence. If you runcompute_vign
you should presssave_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. Works only whenshow_corners
is enabled.show_init_reproj
shows the initial reprojections computed by theinit_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.opt_until_converge
runs the optimization until convergence.stop_thresh
defines the stopping criteria. Optimization will stop when the maximum increment is smaller than this value.
After calibrating the camera you can run the camera + IMU + Mocap calibration. The result path should point to the same folder as before:
granite_calibrate_imu --dataset-path ~/tumvi_calib_data/dataset-calib-imu1_512_16.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --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.
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. Alternatively, you can use theopt_until_converge
checkbox that will run the optimization until it converges automatically.init_mocap
initializes the transformation from the Aprilgrid calibration pattern to the Mocap coordinate system.save_calib
save the current calibration ascalibration.json
in the result folder.save_mocap_calib
save the current Mocap to IMU calibration asmocap_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 contained in the dataset.show_accel
shows accelerometer data.show_gyro
shows gyroscope data.show_pos
shows spline position forshow_spline
and positions generated from camera pose initialization transformed into IMU coordinate frame forshow_data
.show_rot_error
shows the rotation error between spline and camera pose initializations transformed into IMU coordinate frame.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 withopt_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 misalignment 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 pressinginit_mocap
.huber_thresh
controls the threshold for the huber norm in pixels for the optimization.opt_until_convg
runs the optimization until convergence.stop_thresh
defines the stopping criteria. Optimization will stop when the maximum increment is smaller than this value.
NOTE: In this case we use a 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
.
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
Run the camera calibration:
granite_calibrate --dataset-path ~/euroc_calib_data/cam_april.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --result-path ~/euroc_calib_result/ --cam-types ds ds
After calibrating the camera you can run the camera + IMU calibration. The result-path should point to the same folder as before:
granite_calibrate_imu --dataset-path ~/euroc_calib_data/imu_april.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --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
Download the datasets for camera and camera-IMU calibration:
mkdir ~/uzh_calib_data
cd ~/uzh_calib_data
wget http://rpg.ifi.uzh.ch/datasets/uzh-fpv/calib/indoor_forward_calib_snapdragon_cam.bag
wget http://rpg.ifi.uzh.ch/datasets/uzh-fpv/calib/indoor_forward_calib_snapdragon_imu.bag
Run the camera calibration:
granite_calibrate --dataset-path ~/uzh_calib_data/indoor_forward_calib_snapdragon_cam.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_5x4_uzh.json --result-path ~/uzh_calib_result/ --cam-types ds ds
After calibrating the camera you can run the camera + IMU calibration. The result-path should point to the same folder as before:
granite_calibrate_imu --dataset-path ~/uzh_calib_data/indoor_forward_calib_snapdragon_imu.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_5x4_uzh.json --result-path ~/uzh_calib_result/ --gyro-noise-std 0.05 --accel-noise-std 0.1 --gyro-bias-std 4e-5 --accel-bias-std 0.002
Download the datasets for camera and camera-IMU calibration from here (Sample datasets):
mkdir ~/kalibr_calib_data
cd ~/kalibr_calib_data
# Download data
tar xvf static.tar.gz
tar xvf dynamic.tar.gz
Run the camera calibration:
granite_calibrate --dataset-path ~/kalibr_calib_data/static/static.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --result-path ~/kalibr_calib_result/ --cam-types ds ds ds ds
After calibrating the camera you can run the camera + IMU calibration. The result-path should point to the same folder as before:
granite_calibrate_imu --dataset-path ~/kalibr_calib_data/dynamic/dynamic.bag --dataset-type bag --aprilgrid /usr/etc/granite/aprilgrid_6x6.json --result-path ~/kalibr_calib_result/ --gyro-noise-std 0.005 --accel-noise-std 0.01 --gyro-bias-std 4.0e-06 --accel-bias-std 0.0002