ubuntu 18.04 OAK-D kalibr 双目+imu联合标定 ============ 准备 ---------- * 环境干净的ubuntu18.04系统 * OAK-D + USB3.0数据线 (或者是其他支持双目+imu的OAK相机) * 打印标定板(这里使用的是Aprilgrid)， `下载地址 `__ * 标定板配置文件，根据实际打印大小填写到april_6x6.yaml .. code-block:: yaml #example for aprilgrid target_type: 'aprilgrid' #gridtype tagCols: 6 #number of apriltags tagRows: 6 #number of apriltags tagSize: 0.022 #size of apriltag, edge to edge [m] tagSpacing: 0.3 #ratio of space between tags to tagSize #example: tagSize=2m, spacing=0.5m --> tagSpacing=0.25[-] #example for checkerboard # targetType: 'checkerboard' #gridtype # targetCols: 6 #number of internal chessboard corners # targetRows: 7 #number of internal chessboard corners # rowsMetricSize: 0.06 #size of one chessboard square [m] # colsMetricSize: 0.06 #size of one chessboard square [m] 安装相关程序 ---------- ROS以及depthai_ros环境 ^^^^^^^^^^^ ROS melodic + depthai_ros :ref:`教程链接 ` 注意替换ROS版本号 安装标定工具以及依赖 ^^^^^^^^^^^ 编译kalibr """""""""""" .. code-block:: bash sudo apt install libsuitesparse-dev sudo apt install libv4l-dev `源码地址 `__ ，克隆源码到src目录下，然后在kalibr_ws工作目录下使用catkin_make命令编译 .. image:: /_static/images/ros_oak_d_kalibr/kalibr.png 以下是标定时需要的依赖 .. code-block:: bash sudo apt-get install python-igraph python -m pip install scipy 安装Ceres Solver """""""""""" 按照 `官方教程 `__ 安装Ceres Solver 编译code_utils """""""""""" .. code-block:: bash sudo apt install libdw-dev `源码地址 `__ ,克隆源码到src目录下 在code_utils下面找到sumpixel_test.cpp，修改#include "backward.hpp"为 #include “code_utils/backward.hpp”，然后在kalibr_ws工作目录下使用catkin_make命令编译 .. image:: /_static/images/ros_oak_d_kalibr/code_utils.png 编译imu_utils """""""""""" `源码地址 `__ ，克隆源码到src目录下，然后在kalibr_ws工作目录下使用catkin_make命令编译 .. image:: /_static/images/ros_oak_d_kalibr/imu_utils.png 标定过程 ---------- 采集数据 ^^^^^^^^^^^ 启动depthai_examples stereo_inertial_node.launch，这里设置的图像输出帧率是4帧，这样可以让计算量小一点 .. code-block:: bash cd dai_ws source devel/setup.bash roslaunch depthai_examples stereo_inertial_node.launch enableRviz:=false depth_aligned:=false stereo_fps:=4 .. code-block:: bash rostopic list .. image:: /_static/images/ros_oak_d_kalibr/rostopic_list.png 使用到以上图中画框的话题 双目 """""""""""" .. note:: 录制双目数据要在白色背景下录制，之后的双目+imu也是 具体录制方法参考 `视频 `__ .. code-block:: bash rosbag record /stereo_inertial_publisher/left/image_rect /stereo_inertial_publisher/right/image_rect -O stereo.bag imu """""""""""" .. note:: 录制imu数据时相机静止，时间要大于2个小时 .. code-block:: bash rosbag record /stereo_inertial_publisher/imu -O imu.bag 双目+imu """""""""""" 具体录制方法参考 `视频 `__ .. code-block:: bash rosbag record /stereo_inertial_publisher/imu /stereo_inertial_publisher/left/image_rect /stereo_inertial_publisher/right/image_rect -O stereo_imu.bag 标定 ^^^^^^^^^^^ 双目 """""""""""" .. note:: 注意配置文件路径 .. code-block:: bash cd kalibr_ws source devel/setup.bash rosrun kalibr kalibr_calibrate_cameras --bag ../stereo.bag --topics /stereo_inertial_publisher/left/image_rect /stereo_inertial_publisher/right/image_rect --models pinhole-radtan pinhole-radtan --target ../OAK_D/april_6x6.yaml 标定成功，目前可以把重投影误差控制在0.5以内 .. image:: /_static/images/ros_oak_d_kalibr/kalibr_oak_d__stereo_resjpg.jpg 输出cam_chain.yaml .. code-block:: yaml cam0: cam_overlaps: [1] camera_model: pinhole distortion_coeffs: [0.029163490534580016, -0.08332352456606604, -0.001703975799425871, 0.006992997733791862] distortion_model: radtan intrinsics: [814.3568321116179, 815.0894236334714, 683.8491150709657, 340.5856601059704] resolution: [1280, 720] rostopic: /stereo_inertial_publisher/left/image_rect cam1: T_cn_cnm1: - [0.999984764432588, 0.0029265301147959008, 0.004680419231117285, -0.07535219882179961] - [-0.002904105777816229, 0.9999843073810051, -0.004790737036519648, 0.00019253365167533625] - [-0.00469436601929102, 0.004777071614390909, 0.9999775710056993, -0.0005437262712103053] - [0.0, 0.0, 0.0, 1.0] cam_overlaps: [0] camera_model: pinhole distortion_coeffs: [0.013208623179345822, -0.03975319079350225, -0.000241290621102109, 0.008441954380996652] distortion_model: radtan intrinsics: [822.7870689725859, 820.5717606544596, 680.346373177241, 343.830359521292] resolution: [1280, 720] rostopic: /stereo_inertial_publisher/right/image_rect imu """""""""""" oak_d.launch文件内容，放在imu_utils/launch目录下 .. code-block:: launch 播放录制的imu数据，200倍速播放 .. code-block:: bash roscore rosbag play -r 200　imu_utils/imu.bag 启动oak_d.launch .. code-block:: bash cd kalibr_ws source devel/setup.bash roslaunch imu_utils oak_d.launch 输出imu_param.yaml .. code-block:: yaml %YAML:1.0 --- type: IMU name: oak-d Gyr: unit: " rad/s" avg-axis: gyr_n: 5.1659466110355748e-03 gyr_w: 2.3398926139856830e-05 x-axis: gyr_n: 5.9992047089125330e-03 gyr_w: 3.1741702735079302e-05 y-axis: gyr_n: 4.2744238338741254e-03 gyr_w: 1.5610981890151188e-05 z-axis: gyr_n: 5.2242112903200643e-03 gyr_w: 2.2844093794339994e-05 Acc: unit: " m/s^2" avg-axis: acc_n: 2.5112339801819636e-02 acc_w: 5.4434330052722689e-04 x-axis: acc_n: 2.3967193951432327e-02 acc_w: 7.3928376091566368e-04 y-axis: acc_n: 2.2637396692944962e-02 acc_w: 2.7146510845110392e-04 z-axis: acc_n: 2.8732428761081619e-02 acc_w: 6.2228103221491327e-04 双目+imu """""""""""" 需要三个文件：双目+imu的采集数据、根据生成的imu标定结果填写的imu.yaml、双目标定结果 imu.yaml .. code-block:: yaml #Accelerometers accelerometer_noise_density: 2.52e-02 #Noise density (continuous-time) accelerometer_random_walk: 5.44e-04 #Bias random walk #Gyroscopes gyroscope_noise_density: 5.16e-03 #Noise density (continuous-time) gyroscope_random_walk: 2.34e-05 #Bias random walk rostopic: /stereo_inertial_publisher/imu #the IMU ROS topic update_rate: 200.0 #Hz (for discretization of the values above) .. code-block:: bash rosrun kalibr kalibr_calibrate_imu_camera --target april_6x6.yaml --cam stereo_camchain.yaml --imu imu.yaml --bag stereo_imu_oak_d.bag 生成如下结果 .. image:: /_static/images/ros_oak_d_kalibr/oak_d_stereo_imu_kalibr.png camchain-imucam.yaml .. code-block:: yaml cam0: T_cam_imu: - [0.0012741143007174438, -0.999877856617157, -0.015577178159958124, 0.05611936652731515] - [0.9997404060564851, 0.00162798902986383, -0.022725979620935255, 0.00285232424992767] - [0.0227485632680666, -0.01554417892321791, 0.9996203686254297, 0.007183919123961078] - [0.0, 0.0, 0.0, 1.0] cam_overlaps: [1] camera_model: pinhole distortion_coeffs: [0.042268429058025504, -0.08677899665541859, 0.001180600575988654, 0.0022839809291763198] distortion_model: radtan intrinsics: [833.53142215564, 833.6574846171179, 664.9153795944901, 354.05210186777356] resolution: [1280, 720] rostopic: /stereo_inertial_publisher/left/image_rect timeshift_cam_imu: 0.0008209265944781894 cam1: T_cam_imu: - [0.003900423265967401, -0.9999238322965412, -0.011709667106495082, -0.017964937584165064] - [0.9996593071739112, 0.0042010652101436285, -0.0257608352286262, 0.002875608017511033] - [0.025808066160071114, -0.01160519954583952, 0.9995995513527299, 0.011372828202903269] - [0.0, 0.0, 0.0, 1.0] T_cn_cnm1: - [0.999989071372855, 0.002537659366062306, 0.003926502234769603, -0.07411913670569026] - [-0.002525590738806025, 0.9999920811766924, -0.003075544720875524, 0.00018713537154106094] - [-0.003934275826358857, 0.0030655943717139626, 0.9999875617250811, 0.004401043432455365] - [0.0, 0.0, 0.0, 1.0] cam_overlaps: [0] camera_model: pinhole distortion_coeffs: [0.047927948017714446, -0.08886896066637022, 0.001194906027010198, 0.004430080060758361] distortion_model: radtan intrinsics: [846.0352688270785, 845.0776741744443, 659.390274553411, 356.3040770945268] resolution: [1280, 720] rostopic: /stereo_inertial_publisher/right/image_rect timeshift_cam_imu: 0.0008178786423846188 .. include:: /pages/includes/footer-short.rst