Kalibr installation tutorial
Kalibr installation tutorial
I was confused about installing Kalibr, but there is no even one hint in README.md. I just put them in the catkin_ws, in which so many ROS packages are also there. Unsuccessfully, it can't be compiled one by one package by the command catkin_make -DCATKIN_WHITELIST_PACKAGE="PACKAGE_NAME". It means a good choice is to build another ROS workspace in case of rebuilding others in the same workspace.
Resiquite:
sudo apt-get install python-setuptools python-rosinstall ipython libeigen3-dev libboost-all-dev doxygen libopencv-dev ros-kinetic-vision-opencv ros-kinetic-image-transport-plugins ros-kinetic-cmake-modules python-software-properties software-properties-common libpoco-dev python-matplotlib python-scipy python-git python-pip ipython libtbb-dev libblas-dev liblapack-dev python-catkin-tools libv4l-dev
sudo pip install python-igraph --upgrade
Warning: If having done catkin_make at first then must run the following command.
catkin clean -bdy
cd ~
mkdir -p kalibr_ws/src
cd ~/kalibr_ws
source /opt/ros/kinetic/setup.bash
catkin init
catkin config --extend /opt/ros/kinetic
catkin config --merge-devel # Necessary for catkin_tools >= 0.4. catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
cd ~/kalibr_ws/src
git clone https://github.com/ethz-asl/kalibr.git
cd ..
catkin build -DCMAKE_BUILD_TYPE=Release -j4
Output seems like this:
Finished <<< kalibr [ 16.1 seconds ]
[build] Summary: All 37 packages succeeded!
[build] Ignored: None.
[build] Warnings: 21 packages succeeded with warnings.
[build] Abandoned: None.
[build] Failed: None.
[build] Runtime: 14 minutes and 53.4 seconds total.
[build] Note: Workspace packages have changed, please re-source setup files to use them.
source ~/kalibr_ws/devel/setup.bash
Update:
Traceback (most recent call last):
File "../python/kalibr_calibrate_cameras", line 6, in <module>
import sm
ImportError: No module named sm
**Solution: **
sudo pip install sm
then rebuild kalibr.
References:
[1] kalibr教程
[2] Installing and Configuring Your ROS Environment
[3] ethz-asl/kalibr
[4] catkin_make vs catkin build
[5] https://github.com/ethz-asl/kalibr/wiki/installation
[6] 完整版用kalibr标定 camera imu
Multiple camera calibration
roslaunch realsense2_camera rs_camera.launch
rosrun topic_tools throttle messages /camera/color/image_raw 4.0 /color
rosbag record -O rs_cam_hz4 /color
Which distortiong model should be choose for Realsense D435i? From all I know, a factory calibration setup of D435i looks like: (You can /usr/local/bin/rs-sensor-control, type 0, 1, 2, 91 etc to see)
Principal Point : 322.424, 237.813
Focal Length : 617.521, 617.576
Distortion Model : Brown Conrady
Distortion Coefficients : [0,0,0,0,0]
And according to the dorodnic, of course a equidistant distortion model could be used. (But r1 & r2 are needed in realsense comfig in vins. So the best distortion model must be radial-tangential (radtan))
Yes, these are supposed to be zero for the D400. We consider adding coefficient estimation to the RGB calibration to reduce the distortion (by about 1 pixel at extremes), but at the moment projection without coefficients is the most accurate you can do (we are not calibrating and then ignoring the coefficients, we estimate fx, fy, ppx and ppy without them)
cd ~/kalibr_ws/src/kalibr/aslam_offline_calibration/kalibr/data
../python/kalibr_calibrate_cameras --target april_6x6_50x50cm.yaml --bag rs_cam_hz4.bag --models pinhole-equi --topics /color
Note that in the bag file there are up to 800 images, but it only 39. Maybe that's enough for calibration?
Output:
Calibration complete.
[ WARN] [1556719991.003758]: Removed 26 outlier corners.
Processed 826 images with 39 images used
Camera-system parameters:
cam0 (/color):
type: <class 'aslam_cv.libaslam_cv_python.EquidistantDistortedPinholeCameraGeometry'>
distortion: [ 0.3044413 2.04741574 -11.06112629 18.6743852 ] +- [ 0.0320288 0.46759766 2.76374537 5.41971393]
projection: [ 604.9671891 602.10506316 325.8395051 238.35406753] +- [ 10.62286295 10.41921913 1.68531874 1.43868064]
reprojection error: [-0.000000, -0.000000] +- [0.153693, 0.138547]
Results written to file: camchain-rs_cam_hz4.yaml
Detailed results written to file: results-cam-rs_cam_hz4.txt
Result:
camchain-rs_cam_hz4.yaml
cam0:
cam_overlaps: []
camera_model: pinhole
distortion_coeffs: [0.3044413037380324, 2.0474157424478348, -11.061126286843251,
18.67438520203368]
distortion_model: equidistant
intrinsics: [604.9671890973748, 602.1050631617551, 325.83950509989114, 238.35406753467785]
resolution: [640, 480]
rostopic: /color
Compared to the default settings, assumes our result is accurate. The reprojection error seems like good too.
Reference:
[1] Multiple camera calibration
[2] [相机标定]RealSense D435i相机标定
[3] rs2_intrinsics coeffs[] all 0 by default #1430
[4] Camera models
IMU calibration
imu_utils from HKUST
Protecting from error:
CMake Warning at /opt/ros/kinetic/share/catkin/cmake/catkinConfig.cmake:76 (find_package):
Could not find a package configuration file provided by "code_utils" with
any of the following names:
code_utilsConfig.cmake
code_utils-config.cmake
Add the installation prefix of "code_utils" to CMAKE_PREFIX_PATH or set
"code_utils_DIR" to a directory containing one of the above files. If
"code_utils" provides a separate development package or SDK, be sure it has
been installed.
Put code_utils in the workspace, catkin_make first.
Then do the same for imu_utils.
Result (BMI055 is the IMU D435i is using):
BMI055_imu_param.yaml
%YAML:1.0
---
type: IMU
name: BMI055
Gyr:
unit: " rad/s"
avg-axis:
gyr_n: 6.0673370376614875e-03
gyr_w: 3.6211951458325785e-05
x-axis:
gyr_n: 5.4501442406047970e-03
gyr_w: 4.0723401163659986e-05
y-axis:
gyr_n: 5.9380128602687073e-03
gyr_w: 2.9388325769986972e-05
z-axis:
gyr_n: 6.8138540121109601e-03
gyr_w: 3.8524127441330383e-05
Acc:
unit: " m/s^2"
avg-axis:
acc_n: 3.3621979208052800e-02
acc_w: 9.8256589971851467e-04
x-axis:
acc_n: 3.6095477320173631e-02
acc_w: 9.6831827726998488e-04
y-axis:
acc_n: 3.4696437020780901e-02
acc_w: 1.3092042863834673e-03
z-axis:
acc_n: 3.0074023283203882e-02
acc_w: 6.7017513550209160e-04
[1] imu标定 imu_tk
[2] Imu_tk算法流程
[3] catkin_make failed #3
[4] imu_utils
[5] code_utils
camera/IMU calibration
roscd realsense2_camera/
roslaunch realsense2_camera rs_camera.launch
rostopic hz /camera/imu
rostopic hz /camera/color/image_raw
rosrun topic_tools throttle messages /camera/color/image_raw 20.0 /color
rosrun topic_tools throttle messages /camera/imu 200.0 /imu
Some problem:
In the rs_camera.launch, but when I check the frequency: IMU is 150 Hz and the camera is 15FPS. It can't be slow down to the frequency needed.
<arg name="color_fps" default="30"/>
<arg name="gyro_fps" default="200"/> <!-- 200 or 400-->
<arg name="accel_fps" default="250"/> <!-- 63 or 250-->
The best frequency is 200 Hz and 30 Hz. Of course, others are still good.
rosbag record -O rs_cam15hz_imu150hz.bag /color /imu
camchain-rs_cam_hz4.yaml
cam0:
cam_overlaps: []
camera_model: pinhole
distortion_coeffs: [0.3044413037380324, 2.0474157424478348, -11.061126286843251,
18.67438520203368]
distortion_model: equidistant
intrinsics: [604.9671890973748, 602.1050631617551, 325.83950509989114, 238.35406753467785]
resolution: [640, 480]
rostopic: /color
imu.yaml
rostopic: /imu
update_rate: 150.0 #Hz
accelerometer_noise_density: 3.3621979208052800e-02 #continous
accelerometer_random_walk: 9.8256589971851467e-04
gyroscope_noise_density: 6.0673370376614875e-03 #continous
gyroscope_random_walk: 3.6211951458325785e-05
roscd kalibr
cd data
cp ~/catkin_ws/src/realsense/realsense2_camera/rs_cam15hz_imu150hz.bag .
../python/kalibr_calibrate_imu_camera --target april_6x6_50x50cm.yaml --cam camchain-rs_cam_hz4.yaml --imu imu-BMI055.yaml --bag rs_cam15hz_imu150hz.bag
Note that when something is wrong with the input data in bagfile, just record another one bagfile.
Initializing
Optimization problem initialized with 101968 design variables and 1079428 error terms
The Jacobian matrix is 2310198 x 458841
[0.0]: J: 1.35165e+06
Exception in thread block: [aslam::Exception] /home/william/kalibr_ws/src/kalibr/aslam_nonparametric_estimation/aslam_splines/src/BSplineExpressions.cpp:447: toTransformationMatrixImplementation() assert(_bufferTmin <= _time.toScalar() < _bufferTmax) failed [1.55677e+09 <= 1.55677e+09 < 1.55677e+09]: Spline Coefficient Buffer Exceeded. Set larger buffer margins!
Exception in thread block: [aslam::Exception] /home/william/kalibr_ws/src/kalibr/aslam_nonparametric_estimation/aslam_splines/src/BSplineExpressions.cpp:447: toTransformationMatrixImplementation() assert(_bufferTmin <= _time.toScalar() < _bufferTmax) failed [1.55677e+09 <= 1.55677e+09 < 1.55677e+09]: Spline Coefficient Buffer Exceeded. Set larger buffer margins!
Exception in thread block: [aslam::Exception] /home/william/kalibr_ws/src/kalibr/aslam_nonparametric_estimation/aslam_splines/src/BSplineExpressions.cpp:447: toTransformationMatrixImplementation() assert(_bufferTmin <= _time.toScalar() < _bufferTmax) failed [1.55677e+09 <= 1.55677e+09 < 1.55677e+09]: Spline Coefficient Buffer Exceeded. Set larger buffer margins!
[ERROR] [1556773048.921808]: Optimization failed!
Traceback (most recent call last):
File "../python/kalibr_calibrate_imu_camera", line 236, in <module>
main()
File "../python/kalibr_calibrate_imu_camera", line 206, in main
iCal.optimize(maxIterations=parsed.max_iter, recoverCov=parsed.recover_cov)
File "/home/william/kalibr_ws/src/kalibr/aslam_offline_calibration/kalibr/python/kalibr_imu_camera_calibration/IccCalibrator.py", line 179, in optimize
raise RuntimeError("Optimization failed!")
RuntimeError: Optimization failed!
Result looks like this:
After Optimization (Results)
==================
Normalized Residuals
----------------------------
Reprojection error (cam0): mean 0.169417479013, median 0.154212672023, std: 0.0973946838993
Gyroscope error (imu0): mean 0.18574054756, median 0.159830346682, std: 0.115913332564
Accelerometer error (imu0): mean 0.169497068217, median 0.145829709726, std: 0.10939033445
Residuals
----------------------------
Reprojection error (cam0) [px]: mean 0.169417479013, median 0.154212672023, std: 0.0973946838993
Gyroscope error (imu0) [rad/s]: mean 0.013802268496, median 0.0118768970357, std: 0.00861345010194
Accelerometer error (imu0) [m/s^2]: mean 0.0697960902289, median 0.0600502633182, std: 0.0450451310679
Transformation T_cam0_imu0 (imu0 to cam0, T_ci):
[[ 0.01542341 -0.99976267 0.01538561 0.00713584]
[ 0.03147917 -0.01489429 -0.99939343 -0.03487332]
[ 0.9993854 0.01589838 0.03124198 -0.05266484]
[ 0. 0. 0. 1. ]]
cam0 to imu0 time: [s] (t_imu = t_cam + shift)
0.0334634768386
IMU0:
----------------------------
Model: calibrated
Update rate: 150.0
Accelerometer:
Noise density: 0.0336219792081
Noise density (discrete): 0.411783466011
Random walk: 0.000982565899719
Gyroscope:
Noise density: 0.00606733703766
Noise density (discrete): 0.0743093991988
Random walk: 3.62119514583e-05
T_i_b
[[ 1. 0. 0. 0.]
[ 0. 1. 0. 0.]
[ 0. 0. 1. 0.]
[ 0. 0. 0. 1.]]
time offset with respect to IMU0: 0.0 [s]
Saving camera chain calibration to file: camchain-imucam-rs_cam15hz_imu150hz.yaml
Saving imu calibration to file: imu-rs_cam15hz_imu150hz.yaml
Detailed results written to file: results-imucam-rs_cam15hz_imu150hz.txt
Generating result report...
/home/william/kalibr_ws/src/kalibr/Schweizer-Messer/sm_python/python/sm/PlotCollection.py:57: wxPyDeprecationWarning: Using deprecated class PySimpleApp.
app = wx.PySimpleApp()
Report written to report-imucam-rs_cam15hz_imu150hz.pdf
References:
[1] Kalibr 标定双目内外参数以及 IMU 外参数
[2] [相机标定]RealSense D435i相机标定
[3] Problem with single imu and single cam Optimization failed #223
Kalibr installation tutorial的更多相关文章
- HP LoadRunner 12.02 Tutorial T7177-88037教程独家中文版
HP LoadRunner 12.02 Tutorial T7177-88037教程独家中文版 Tylan独家呕血翻译 转载请注明出自“天外归云”的博客园 Welcome to the LoadRun ...
- Python 之 MySQL 操作库 lazy_mysql
TOC Intro Installation Tutorial API Engine Pool Column Table Intro lazy_mysql 是一个非常简单易用,用来操作 MySQL 的 ...
- openbr on linuxmint13/ubuntu12.04/debian7 x64 facial recognition [Compile from source!!!]
Openbr is a great project for facial detecting. System: linuxmint 13 x86_64 Face recognition, motio ...
- linux mint 安装 opencv2.4
Download opencv https://github.com/opencv/opencv/tree/2.4 安装必要的依赖 sudo apt-get install build-essenti ...
- Linux--Introduction and Basic commands(Part one)
Welcome to Linux world! Introduction and Basic commands--Part one J.C 2018.3.11 Chapter 1 What Is Li ...
- LoadRuner12.53教程(三)
教训1:建立一个Vuser Script jiào教 xùn训 1 : jiàn建 lì立 yī一 gè个 V u s e r S c r ...
- Spring Boot Reference Guide
Spring Boot Reference Guide Authors Phillip Webb, Dave Syer, Josh Long, Stéphane Nicoll, Rob Winch, ...
- hbase-indexer官网wiki
Home Requirements Getting Started Installation Tutorial Demo Indexer Configuration CLI tools Metrics ...
- Ubuntu16手动安装OpenStack——glance篇--转
全文转自https://www.voidking.com/dev-ubuntu16-manual-openstack-glance/ 目标 紧接着<Ubuntu16手动安装OpenStack—— ...
随机推荐
- zoj1001-A + B Problem
http://acm.zju.edu.cn/onlinejudge/showProblem.do?problemId=1 A + B Problem Time Limit: 2 Seconds ...
- zoj1037-Gridland
http://acm.zju.edu.cn/onlinejudge/showProblem.do?problemId=37 Gridland Time Limit: 2 Seconds Me ...
- java动态规划取硬币问题
最近一直在研究动态规划的问题.今天遇到了取硬币问题. 其实动态规划还是,我从底部向顶部,依次求出每个状态的最小值,然后就可以标记上. 这道题目就是,假如有1,5,7,10这四种币值的硬币,我取14元, ...
- ubuntu server 12.04 jdk,ssh及hadoop配置
我是在32位的系统下配置的,所以在下载安装文件时候要注意. 第一步:下载并配置JDK 1.下载jdk,这里下载的是jdk1.7.0_65版本的,命令如下 $ wget http://download. ...
- hadoop组件启动和关闭命令
一.启动相关组件之前 一般安装完hadoop之后需要格式化一遍hdfs: hdfs namenode -format 然后再进行其他组件的启动,hadoop相关组件都是用位于...hadoop/sbi ...
- 最近公共祖先 LCA Tarjan算法
来自:http://www.cnblogs.com/ylfdrib/archive/2010/11/03/1867901.html 对于一棵有根树,就会有父亲结点,祖先结点,当然最近公共祖先就是这两个 ...
- 修改字段注释modify
alter table test1 modify 字段名 类型 comment '修改后的字段注释'; ALTER TABLE tc_activity_miaosha MODIFY `validity ...
- Flip
Flip是一个能够让任意HTML.文本或jQuery Element产生漂亮翻转效果的jQuery插件. 可以配置翻转方向:从右到左.上到下或从左到右.下到上.翻转的速度也可以配置. 效果如下图所示: ...
- real-Time Correlative Scan Matching
启发式算法(heuristic algorithm)是相对于最优化算法提出的.一个问题的最优算法求得该问题每个实例的最优解.启发式算法可以这样定义:一个基于直观或经验构造的算法,在可接受的花费(指计算 ...
- 白盒测试实践项目(day1)
由于近期各种考试逼近,我们小组白盒测试实践项目进度有些慢,在任务决定后的两天里,我们小组各个成员的进度完成不一. 胡俊辉熟悉了怎么使用Junit对部分代码的测试,初步掌握了Junit的简单使用. 汪鸿 ...