300 lines
13 KiB
ReStructuredText
300 lines
13 KiB
ReStructuredText
.. _how_to_use_kalibr:
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How to calibrate MYNTEYE by kalibr
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===================================
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Target
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------------
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* Calibrate the pose relationship between left and right camera
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* Calibrate the pose relationship left camera between and IMU
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Preparation
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------------
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* **Install kalibr**:Refer to `kalibr wiki <https://github.com/ethz-asl/kalibr/wiki/installation>`_ and follow the steps to install
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* **Calibration board**： kalibr supports ``chessbord`` , ``circlegrid`` , ``aprilgrid`` ,choose ``aprilgrid`` here Calibration board file can be directly `download <https://github.com/ethz-asl/kalibr/wiki/downloads>`_ , Or you can also generate calibration board by Kalibr tool.
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.. code-block:: bash
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$ kalibr_create_target_pdf --type 'apriltag' --nx 6 --ny 6 --tsize 0.08 --tspace 0.3
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View parameters' meanings by kalibr_create)target_pdf command:
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.. code-block:: bash
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$ kalibr_create_target_pdf --h
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usage:
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Example Aprilgrid:
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kalibr_create_target_pdf --type apriltag --nx 6 --ny 6 --tsize 0.08 --tspace 0.3
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Example Checkerboard:
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kalibr_create_target_pdf --type checkerboard --nx 6 --ny 6 -csx 0.05 --csy 0.1
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Generate PDFs of calibration patterns.
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optional arguments:
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-h, --help show this help message and exit
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Output options:
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output Output filename
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--eps Also output an EPS file
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Generic grid options:
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--type GRIDTYPE The grid pattern type. ('apriltag' or 'checkerboard')
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--nx N_COLS The number of tags in x direction (default: 6)
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--ny N_ROWS The number of tags in y direction (default: 7)
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Apriltag arguments:
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--tsize TSIZE The size of one tag [m] (default: 0.08)
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--tspace TAGSPACING The space between the tags in fraction of the edge size
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[0..1] (default: 0.3)
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--tfam TAGFAMILIY Familiy of April tags ['t16h5', 't25h9', 't25h7',
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't36h11'] (default: t36h11)
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Checkerboard arguments:
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--csx CHESSSZX The size of one chessboard square in x direction [m]
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(default: 0.05)
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--csy CHESSSZY The size of one chessboard square in y direction [m]
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(default: 0.05)
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* **Calibrate the intrinsic IMU parameters** ：kalibr requires imu data to be calibrated by intrinsic parameters by default.The intrinsic parameters calibration tool uses `imu-tk <https://github.com/Kyle-ak/imu_tk.git>`_ .
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* **Count imu data parameter**：
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* noise density
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* bias random walk
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Using Allan analyzing tool `imu_utils <https://github.com/gaowenliang/imu_utils>`_, We can get the characteristics of above imu data,and to format the output as ``imu.yaml``
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.. code-block:: bash
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#Accelerometers
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accelerometer_noise_density: 0.02680146180736048 #Noise density (continuous-time)
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accelerometer_random_walk: 0.0026296086159332804 #Bias random walk
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#Gyroscopes
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gyroscope_noise_density: 0.008882328296710996 #Noise density (continuous-time)
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gyroscope_random_walk: 0.00037956578292701033 #Bias random walk
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rostopic: /mynteye/imu/data_raw #the IMU ROS topic
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update_rate: 200.0 #Hz (for discretization of the values above)
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Calibrate the pose relationship between left and right camera
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--------------------------------------------------------------
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* Collect calibration images: kalibr supports the collection of the required calibration images through two ways:by ``rosbag`` or collect offline images . Using ``rosbag`` here for convenience,Reference `link <https://github.com/ethz-asl/kalibr/wiki/bag-format>`_ for collecting images.
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* Method of collecting images by ``rosbag`` :fix mynteye camera,move ``aprilgrid`` calibration board in the camera field of view.
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* To increase the calibration time,try to use image acquisition data with lower frame rate,kalibr recommends using ``4Hz`` frame rate,here uses ``10hz`` .
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* MYNTEYE S series camera offers images at least 10Hz,You can use `topic_tools <http://wiki.ros.org/topic_tools/throttle>`_ to modify frequency,because using 10Hz requires more calibration time.
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* Record ``static.bag`` : After fix the mynteye camera,start `wrapper <https://github.com/slightech/MYNT-EYE-S-SDK>`_, record the topic of the left and right images to ``static.bag`` .
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.. code-block:: bash
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$ source wrappers/ros/devel/setup.bash
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$ roslaunch mynt_eye_ros_wrapper display.launch
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$ cd ~
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$ mkdir -p bag
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$ cd bag
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$ rosbag record -O static_10hz /mynteye/left/image_raw /mynteye/right/image_raw #recommand use 10hz,you can also use topic_tools to publish 4hz.
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* kalibr calibration:
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.. code-block:: bash
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$ kalibr_calibrate_cameras --target aprilgrid.yaml --bag ~/bag/static_10hz.bag --models pinhole-radtan pinhole-radtan --topics /mynteye/left/image_raw /mynteye/right/image_raw
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View parameters' meanings by kalibr_calibrate_cameras command:
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.. code-block:: bash
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$ kalibr_calibrate_cameras --h
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Calibrate the intrinsics and extrinsics of a camera system with non-shared
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overlapping field of view.
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usage:
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Example usage to calibrate a camera system with two cameras using an aprilgrid.
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cam0: omnidirection model with radial-tangential distortion
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cam1: pinhole model with equidistant distortion
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kalibr_calibrate_cameras --models omni-radtan pinhole-equi --target aprilgrid.yaml \
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--bag MYROSBAG.bag --topics /cam0/image_raw /cam1/image_raw
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example aprilgrid.yaml:
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target_type: 'aprilgrid'
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tagCols: 6
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tagRows: 6
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tagSize: 0.088 #m
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tagSpacing: 0.3 #percent of tagSize
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optional arguments:
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-h, --help show this help message and exit
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--models MODELS [MODELS ...]
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The camera model ['pinhole-radtan', 'pinhole-equi',
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'omni-radtan', 'pinhole-fov'] to estimate
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Data source:
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--bag BAGFILE The bag file with the data
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--topics TOPICS [TOPICS ...]
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The list of image topics
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--bag-from-to bag_from_to bag_from_to
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Use the bag data starting from up to this time [s]
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Calibration target configuration:
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--target TARGETYAML Calibration target configuration as yaml file
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Image synchronization:
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--approx-sync MAX_DELTA_APPROXSYNC
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Time tolerance for approximate image synchronization
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[s] (default: 0.02)
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Calibrator settings:
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--qr-tol QRTOL The tolerance on the factors of the QR decomposition
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(default: 0.02)
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--mi-tol MITOL The tolerance on the mutual information for adding an
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image. Higher means fewer images will be added. Use -1
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to force all images. (default: 0.2)
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--no-shuffle Do not shuffle the dataset processing order
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Outlier filtering options:
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--no-outliers-removal
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Disable corner outlier filtering
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--no-final-filtering Disable filtering after all views have been processed.
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--min-views-outlier MINVIEWOUTLIER
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Number of raw views to initialize statistics (default:
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20)
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--use-blakezisserman Enable the Blake-Zisserman m-estimator
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--plot-outliers Plot the detect outliers during extraction (this could
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be slow)
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Output options:
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--verbose Enable (really) verbose output (disables plots)
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--show-extraction Show the calibration target extraction. (disables
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plots)
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--plot Plot during calibration (this could be slow).
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--dont-show-report Do not show the report on screen after calibration.
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Output the following three files after finish calibration:
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* ``camchain-homezhangsbagstatic_10hz.yaml``
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* ``report-cam-homezhangsbagstatic_10hz.pdf``
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* ``results-cam-homezhangsbagstatic_10hz.txt``
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.. tip::
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If you use camera parameters in Vins,it would be better to choose the pinhole-equi model or the omni-radtan model.If you use camera parameters in Maplab,please choose pinhole-equi model
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Calibrate the pose relationship between camera and IMU coordinate system
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-------------------------------------------------------------------------
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* **Collect calibration data**：as calibrate the pose relationship of camera,Kalibr supports two ways to collect data,we still use ``rosbag`` here.
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* Method of collecting image: fix ``apilgrid`` calibration board, move camera
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* Make sure that the data collected is good:the brightness of the calibration board should be appropriate,too bright or too dark can't guarantee the quality of data,meanwhile do not shake too fast to avoid blurring of the image.
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* Set the imu publishing frequency to 200Hz,image to 20Hz(recommended by kalibr)
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* Fully motivate each axis of the imu,for example ,3 actions on each axis,then in the \"8-shaped\" motion
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* Record camera and imu as ``dynamic.bag``.
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.. code-block:: bash
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$ roslaunch mynt_eye_ros_wrapper display.launch
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$ cd bag
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$ rosbag record -O dynamic /mynteye/left/image_raw /mynteye/right/image_raw /mynteye/imu/data_raw #remember set image hz to 20hz, imu hz to 200hz
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* kalibr calibration:
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.. code-block:: bash
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$ kalibr_calibrate_imu_camera --cam camchain-homezhangsbagstatic_10hz.yaml --target aprilgrid.yaml --imu imu.yaml --time-calibration --bag ~/bag/dynamic.bag
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View the parameters' meanings by kalibr_calibrate_imu_camera command
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.. code-block:: bash
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$ kalibr_calibrate_imu_camera --h
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Calibrate the spatial and temporal parameters of an IMU to a camera chain.
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usage:
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Example usage to calibrate a camera system against an IMU using an aprilgrid
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with temporal calibration enabled.
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kalibr_calibrate_imu_camera --bag MYROSBAG.bag --cam camchain.yaml --imu imu.yaml \
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--target aprilgrid.yaml --time-calibration
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camchain.yaml: is the camera-system calibration output of the multiple-camera
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calibratin tool (kalibr_calibrate_cameras)
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example aprilgrid.yaml: | example imu.yaml: (ADIS16448)
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target_type: 'aprilgrid' | accelerometer_noise_density: 0.006
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tagCols: 6 | accelerometer_random_walk: 0.0002
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tagRows: 6 | gyroscope_noise_density: 0.0004
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tagSize: 0.088 | gyroscope_random_walk: 4.0e-06
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tagSpacing: 0.3 | update_rate: 200.0
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optional arguments:
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-h, --help show this help message and exit
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Dataset source:
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--bag BAGFILE Ros bag file containing image and imu data (rostopics
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specified in the yamls)
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--bag-from-to bag_from_to bag_from_to
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Use the bag data starting from up to this time [s]
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--perform-synchronization
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Perform a clock synchronization according to 'Clock
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synchronization algorithms for network measurements'
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by Zhang et al. (2002).
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Camera system configuration:
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--cams CHAIN_YAML Camera system configuration as yaml file
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--recompute-camera-chain-extrinsics
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Recompute the camera chain extrinsics. This option is
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exclusively recommended for debugging purposes in
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order to identify problems with the camera chain
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extrinsics.
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--reprojection-sigma REPROJECTION_SIGMA
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Standard deviation of the distribution of reprojected
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corner points [px]. (default: 1.0)
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IMU configuration:
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--imu IMU_YAMLS [IMU_YAMLS ...]
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Yaml files holding the IMU noise parameters. The first
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IMU will be the reference IMU.
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--imu-delay-by-correlation
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Estimate the delay between multiple IMUs by
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correlation. By default, no temporal calibration
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between IMUs will be performed.
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--imu-models IMU_MODELS [IMU_MODELS ...]
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The IMU models to estimate. Currently supported are
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'calibrated', 'scale-misalignment' and 'scale-
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misalignment-size-effect'.
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Calibration target:
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--target TARGET_YAML Calibration target configuration as yaml file
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Optimization options:
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--time-calibration Enable the temporal calibration
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--max-iter MAX_ITER Max. iterations (default: 30)
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--recover-covariance Recover the covariance of the design variables.
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--timeoffset-padding TIMEOFFSET_PADDING
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Maximum range in which the timeoffset may change
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during estimation [s] (default: 0.01)
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Output options:
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--show-extraction Show the calibration target extraction. (disables
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plots)
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--extraction-stepping
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Show each image during calibration target extraction
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(disables plots)
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--verbose Verbose output (disables plots)
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--dont-show-report Do not show the report on screen after calibration.
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Output the follwing 4 files after finish calibration:
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* ``camchain-imucam-homezhangsbagdynamic.yaml``
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* ``imu-homezhangsbagdynamatic.yaml``
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* ``report-imucam-homezhangsbagdynamic.pdf``
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* ``results-imucam-homezhangsbagdynamic.yaml`` |