如何用 kalibr 标定 MYNTEYE¶
标定目标¶
- 标定左右目之间的位姿关系
- 标定相机（左目）和IMU坐标系之间的位姿关系
准备工作¶
- 安装kalibr： 参考 kalibr wiki ，按步骤安装
- 标定板： kalibr支持
chessbord,circlegrid,aprilgrid， 这里选择aprilgrid,标定板文件可以通过直接 下载 ，或者通过kalibr工具生成标定板:
$ kalibr_create_target_pdf --type 'apriltag' --nx 6 --ny 6 --tsize 0.08 --tspace 0.3
通过该命令查看kalibr_create_target_pdf参数意义：
$ kalibr_create_target_pdf --h
usage:
Example Aprilgrid:
kalibr_create_target_pdf --type apriltag --nx 6 --ny 6 --tsize 0.08 --tspace 0.3
Example Checkerboard:
kalibr_create_target_pdf --type checkerboard --nx 6 --ny 6 -csx 0.05 --csy 0.1
Generate PDFs of calibration patterns.
optional arguments:
-h, --help show this help message and exit
Output options:
output Output filename
--eps Also output an EPS file
Generic grid options:
--type GRIDTYPE The grid pattern type. ('apriltag' or 'checkerboard')
--nx N_COLS The number of tags in x direction (default: 6)
--ny N_ROWS The number of tags in y direction (default: 7)
Apriltag arguments:
--tsize TSIZE The size of one tag [m] (default: 0.08)
--tspace TAGSPACING The space between the tags in fraction of the edge size
[0..1] (default: 0.3)
--tfam TAGFAMILIY Familiy of April tags ['t16h5', 't25h9', 't25h7',
't36h11'] (default: t36h11)
Checkerboard arguments:
--csx CHESSSZX The size of one chessboard square in x direction [m]
(default: 0.05)
--csy CHESSSZY The size of one chessboard square in y direction [m]
(default: 0.05)
- 标定imu内参 ：kalibr默认情况下要求imu数据是经过内参标定的。内参标定工具使用 imu-tk .
- imu 统计数据参数：
- noise density
- bias random walk
我们使用 Allan分析工具 imu_utils 得到上面的imu统计数据特性，并以 如下 imu.yaml 格式化输出：
#Accelerometers
accelerometer_noise_density: 0.02680146180736048 #Noise density (continuous-time)
accelerometer_random_walk: 0.0026296086159332804 #Bias random walk
#Gyroscopes
gyroscope_noise_density: 0.008882328296710996 #Noise density (continuous-time)
gyroscope_random_walk: 0.00037956578292701033 #Bias random walk
rostopic: /mynteye/imu/data_raw #the IMU ROS topic
update_rate: 200.0 #Hz (for discretization of the values above)
标定左右目之间的位姿关系¶
- 收集标定图像：kalibr支持通过
rosbag和采集离线图像两种方式收集所需要的标定图像，这里使用更加方便的rosbag的方式，采集图像的方式 参考链接 . - 通过
rosbag采集图像的方法：固定小觅相机，在相机视野中移动aprilgrid标定板. - 为了避免采集数据的冗余，增加标定的时间， 应尽量使用更低帧率的图像采集数据，kalibr推荐使用
4Hz图像帧率,这里使用10hz. - 小觅相机s系列提供最低10hz的图像，可以使用 topic_tools 修改发布频率,因为使用10hz需要更多的标定时间.
- 录制
static.bag: 固定好小觅相机后，启动 wrapper ,录制左右图像的topic到static.bag.
$ source wrappers/ros/devel/setup.bash
$ roslaunch mynt_eye_ros_wrapper display.launch
$ cd ~
$ mkdir -p bag
$ cd bag
$ rosbag record -O static_10hz /mynteye/left/image_raw /mynteye/right/image_raw #建议使用10hz,你也可以使用topic_tools发布4hz.
- kalibr标定：
$ 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
通过该命令帮助查看kalibr_calibrate_cameras参数意义：
$ kalibr_calibrate_cameras --h
Calibrate the intrinsics and extrinsics of a camera system with non-shared
overlapping field of view.
usage:
Example usage to calibrate a camera system with two cameras using an aprilgrid.
cam0: omnidirection model with radial-tangential distortion
cam1: pinhole model with equidistant distortion
kalibr_calibrate_cameras --models omni-radtan pinhole-equi --target aprilgrid.yaml \
--bag MYROSBAG.bag --topics /cam0/image_raw /cam1/image_raw
example aprilgrid.yaml:
target_type: 'aprilgrid'
tagCols: 6
tagRows: 6
tagSize: 0.088 #m
tagSpacing: 0.3 #percent of tagSize
optional arguments:
-h, --help show this help message and exit
--models MODELS [MODELS ...]
The camera model ['pinhole-radtan', 'pinhole-equi',
'omni-radtan', 'pinhole-fov'] to estimate
Data source:
--bag BAGFILE The bag file with the data
--topics TOPICS [TOPICS ...]
The list of image topics
--bag-from-to bag_from_to bag_from_to
Use the bag data starting from up to this time [s]
Calibration target configuration:
--target TARGETYAML Calibration target configuration as yaml file
Image synchronization:
--approx-sync MAX_DELTA_APPROXSYNC
Time tolerance for approximate image synchronization
[s] (default: 0.02)
Calibrator settings:
--qr-tol QRTOL The tolerance on the factors of the QR decomposition
(default: 0.02)
--mi-tol MITOL The tolerance on the mutual information for adding an
image. Higher means fewer images will be added. Use -1
to force all images. (default: 0.2)
--no-shuffle Do not shuffle the dataset processing order
Outlier filtering options:
--no-outliers-removal
Disable corner outlier filtering
--no-final-filtering Disable filtering after all views have been processed.
--min-views-outlier MINVIEWOUTLIER
Number of raw views to initialize statistics (default:
20)
--use-blakezisserman Enable the Blake-Zisserman m-estimator
--plot-outliers Plot the detect outliers during extraction (this could
be slow)
Output options:
--verbose Enable (really) verbose output (disables plots)
--show-extraction Show the calibration target extraction. (disables
plots)
--plot Plot during calibration (this could be slow).
--dont-show-report Do not show the report on screen after calibration.
标定完成后输出下面3个文件:
camchain-homezhangsbagstatic_10hz.yamlreport-cam-homezhangsbagstatic_10hz.pdfresults-cam-homezhangsbagstatic_10hz.txt
小技巧
如果您在vins中使用相机参数，选择pinhole-equi 模型 或者 omni-radtan模型效果会更好, 如果您在maplab中使用相机参数，请选择pinhole-equi模型
标定相机和IMU坐标系之间的位姿关系¶
- 收集标定数据：跟上面标定相机之间的位姿关系一样，kalibr支持两种收集数据的方法,这里仍然使用
rosbag的方式. - 采集图像的方法： 固定
apilgrid标定板,移动相机 - 保证采集数据是良好的： 标定板亮度要适宜，太亮或太暗不能保证数据的质量, 同时也不要晃动太快，避免图像出现模糊.
- 设置imu发布频率为200Hz, 图像的发布频率为20hz（kalibr推荐）
- 充分激励imu的每个轴，例如可以在每个轴上3个动作，然后在“８字型”运动
- 采集图像的方法： 固定
- 收集标定数据：跟上面标定相机之间的位姿关系一样，kalibr支持两种收集数据的方法,这里仍然使用
- 录制相机与imu的bag为
dynamic.bag
$ roslaunch mynt_eye_ros_wrapper display.launch
$ cd bag
$ rosbag record -O dynamic /mynteye/left/image_raw /mynteye/right/image_raw /mynteye/imu/data_raw #注意设置图像发布频率为20hz, imu发布频率为200hz
- kalibr 标定：
$ kalibr_calibrate_imu_camera --cam camchain-homezhangsbagstatic_10hz.yaml --target aprilgrid.yaml --imu imu.yaml --time-calibration --bag ~/bag/dynamic.bag
通过该命令帮助查看kalibr_calibrate_imu_camera参数意义：
$ kalibr_calibrate_imu_camera --h
Calibrate the spatial and temporal parameters of an IMU to a camera chain.
usage:
Example usage to calibrate a camera system against an IMU using an aprilgrid
with temporal calibration enabled.
kalibr_calibrate_imu_camera --bag MYROSBAG.bag --cam camchain.yaml --imu imu.yaml \
--target aprilgrid.yaml --time-calibration
camchain.yaml: is the camera-system calibration output of the multiple-camera
calibratin tool (kalibr_calibrate_cameras)
example aprilgrid.yaml: | example imu.yaml: (ADIS16448)
target_type: 'aprilgrid' | accelerometer_noise_density: 0.006
tagCols: 6 | accelerometer_random_walk: 0.0002
tagRows: 6 | gyroscope_noise_density: 0.0004
tagSize: 0.088 | gyroscope_random_walk: 4.0e-06
tagSpacing: 0.3 | update_rate: 200.0
optional arguments:
-h, --help show this help message and exit
Dataset source:
--bag BAGFILE Ros bag file containing image and imu data (rostopics
specified in the yamls)
--bag-from-to bag_from_to bag_from_to
Use the bag data starting from up to this time [s]
--perform-synchronization
Perform a clock synchronization according to 'Clock
synchronization algorithms for network measurements'
by Zhang et al. (2002).
Camera system configuration:
--cams CHAIN_YAML Camera system configuration as yaml file
--recompute-camera-chain-extrinsics
Recompute the camera chain extrinsics. This option is
exclusively recommended for debugging purposes in
order to identify problems with the camera chain
extrinsics.
--reprojection-sigma REPROJECTION_SIGMA
Standard deviation of the distribution of reprojected
corner points [px]. (default: 1.0)
IMU configuration:
--imu IMU_YAMLS [IMU_YAMLS ...]
Yaml files holding the IMU noise parameters. The first
IMU will be the reference IMU.
--imu-delay-by-correlation
Estimate the delay between multiple IMUs by
correlation. By default, no temporal calibration
between IMUs will be performed.
--imu-models IMU_MODELS [IMU_MODELS ...]
The IMU models to estimate. Currently supported are
'calibrated', 'scale-misalignment' and 'scale-
misalignment-size-effect'.
Calibration target:
--target TARGET_YAML Calibration target configuration as yaml file
Optimization options:
--time-calibration Enable the temporal calibration
--max-iter MAX_ITER Max. iterations (default: 30)
--recover-covariance Recover the covariance of the design variables.
--timeoffset-padding TIMEOFFSET_PADDING
Maximum range in which the timeoffset may change
during estimation [s] (default: 0.01)
Output options:
--show-extraction Show the calibration target extraction. (disables
plots)
--extraction-stepping
Show each image during calibration target extraction
(disables plots)
--verbose Verbose output (disables plots)
--dont-show-report Do not show the report on screen after calibration.
- 标定完成后输出下面4个文件：
camchain-imucam-homezhangsbagdynamic.yamlimu-homezhangsbagdynamatic.yamlreport-imucam-homezhangsbagdynamic.pdfresults-imucam-homezhangsbagdynamic.yaml