在ros环境下的RealsenceT265标定以及Vins mono运行

转载自：https://mp.weixin.qq.com/s/U4gY7l-YV3sp8-thJrIlUg

在ros环境下的RealsenceT265标定以及Vins mono运行

原创 lovely_yoshino 古月居 今天

相机标定

对于t265而言，虽然官方提供了标定的出厂内参rs-enumerate-devices -c可以获得，但是我们在vins和orbslam中仍然有可能需要自己标定数据，这里给出教程。

首先我们要明确适用的模型，一般普通相机小孔模型即可，而鱼眼镜头则是适用KB4(Kannala-Brandt Camera Model)或者Mei模型。

目前可以用kalibr或者vins-fusion里面的camera-calibration 也可以用basalt。首先我们需要明确basalt必须使用AprilTag，而kalibr则是可以使用AprilTag、Checkerboard、Crilegrid这三种标定板。

下面我们将从kalibr 和 basalt来实现标定操作。

kalibr

首先安装依赖库：

sudo apt-get install python-setuptoolssudo apt-get install python-setuptools python-rosinstall ipython libeigen3-dev libboost-all-dev doxygensudo apt-get install 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

接下来安装kalibr：

cd ~/catkin_ws/srcgit clone https://github.com/ethz-asl/Kalibr.gitcd ..catkin_makesource ~/catkin_ws/devel/setup.bash

在看其他博客时发现可能会出现python相关的问题，我用的是ubuntu16.04自带的Python 2.7.12版本，在安装过程中没有出现问题。

下载官方给的april_6x6_80x80cm_A0.pdf或者其它标定文件。打印或者在屏幕显示，测量实际的尺寸后，在你之前新建的文件夹中新建apriltags.yaml，我的文件内容如下：

(*https://github.com/MegviiRobot/CamLaserCalibraTool/blob/master/doc/april_6x6_80x80cm_A0.pdf)

target_type: 'aprilgrid' #gridtypetagCols: 6               #number of apriltagstagRows: 6               #number of apriltagstagSize: 0.16           #size of apriltag, edge to edge [m]tagSpacing: 0.3125         #ratio of space between tags to tagSize                         #example: tagSize=2m, spacing=0.5m --> tagSpacing=0.25[-]

之后，在你新建的文件夹中打开终端，开启realsenseT265：

roslaunch realsense2_camera rs_t265.launch

修改话题发布频率：

rosrun topic_tools throttle messages /camera/fisheye2/image_raw 10.0 /fisheye2rosrun topic_tools throttle messages /camera/fisheye1/image_raw 10.0 /fisheye1

录制文件，注意录制过程中要缓慢移动相机，使其能看到完整清晰的标定文件（可以先在录制前打开rviz，调用image的话题进行观察，判断移动的位置）：

rosbag record -O cameras_calibration /fisheye1 /fisheye2

在录制好后使用下面的指令输出：

kalibr_calibrate_cameras --target ~/cal/apriltags.yaml --bag  ~/cal/cameras_calibration.bag --models omni-radtan omni-radtan --topics /fisheye1 /fisheye2  basalt_calibrate --dataset-path ~/cal/cameras_calibration.bag --dataset-type bag --aprilgrid ~/cal/apriltags.json --result-path ~/cal/ --cam-types ds ds ds ds

此外可以参考这两篇文章。(*https://blog.csdn.net/lemonxiaoxiao/article/details/109369181)

(*https://blog.csdn.net/HUST_lc/article/details/96144499)

IMU标定，以及联合

(*https://blog.csdn.net/weixin_44631150/article/details/104495156)

basalt

首先我们将会创建一个名为apriltags.json

{  "target_type": "checkerboard",  "tagCols": 11,  "tagRows": 8,  "tagSize": 0.03,  "tagSpacing": 0.3}

之后，在你新建的文件夹中打开终端，开启realsenseT265：

roslaunch realsense2_camera rs_t265.launch

修改话题发布频率：

rosrun topic_tools throttle messages /camera/fisheye2/image_raw 10.0 /fisheye2rosrun topic_tools throttle messages /camera/fisheye1/image_raw 10.0 /fisheye1

录制文件，注意录制过程中要缓慢移动相机，使其能看到完整清晰的标定文件（可以先在录制前打开rviz，调用image的话题进行观察，判断移动的位置）：

rosbag record -O cameras_calibration /fisheye1 /fisheye2

在录制好后使用下面的指令输出(ds代表摄像头，有几个摄像头就有几个ds，ds是使用的模型，如果使用的是kb模型，则在后面填写kb4)：

basalt_calibrate --dataset-path ~/cal/cameras_calibration.bag --dataset-type bag --aprilgrid ~/cal/apriltags.json --result-path ~/cal/ --cam-types ds ds or basalt_calibrate --dataset-path ~/cal/cameras_calibration.bag --dataset-type bag --aprilgrid ~/cal/apriltags.json --result-path ~/cal/t265 --cam-types kb4 kb4

然后即可标定。

陪你去看流星雨

目前网上的教程对T265的信息较少，而对于vins mono的使用则更少，为此我们针对vins mono来进行阐述。

首先，我们需要先安装好VINS-Mono并且在数据集上正常工作。(*https://github.com/HKUST-Aerial-Robotics/VINS-Mono)

在VINS-Fusion/config/realsence文件夹中,修改realsense_fisheye_config.yaml文件至如下：（具体修改可以参照文章）

(*https://blog.csdn.net/qq_41839222/article/details/86552367)

%YAML:1.0 #common parametersimu_topic: "/camera/imu"image_topic: "/camera/fisheye1/image_raw"output_path: "~/output/" #camera calibration model_type: KANNALA_BRANDTcamera_name: cameraimage_width: 848image_height: 800projection_parameters:   k2: -0.00484799   k3: 0.0389167   k4: -0.0367587   k5: 0.00608432   u0: 423.719   v0: 404.247   mu: 286.896   mv: 286.717 # Extrinsic parameter between IMU and Camera.estimate_extrinsic: 0   # 0  Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.                        # 1  Have an initial guess about extrinsic parameters. We will optimize around your initial guess.                        # 2  Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning.                        #If you choose 0 or 1, you should write down the following matrix.#Rotation from camera frame to imu frame, imu^R_camextrinsicRotation: !!opencv-matrix   rows: 3   cols: 3   dt: d   data: [ -0.999891,  -0.0104525,  0.0104421,           0.0104582,  -0.999945,  0.000490038,            0.0104365,  0.000599191,  0.999945]#Translation from camera frame to imu frame, imu^T_camextrinsicTranslation: !!opencv-matrix   rows: 3   cols: 1   dt: d   data: [0.0106988288462162,  -0.000111902991193347,  -0.000111670000478625] #feature traker paprametersmax_cnt: 150            # max feature number in feature trackingmin_dist: 30            # min distance between two features freq: 10                # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image F_threshold: 1.0        # ransac threshold (pixel)show_track: 1           # publish tracking image as topicequalize: 0             # if image is too dark or light, trun on equalize to find enough featuresfisheye: 0              # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points #optimization parametersmax_solver_time: 0.04  # max solver itration time (ms), to guarantee real timemax_num_iterations: 12   # max solver itrations, to guarantee real timekeyframe_parallax: 10.0 # keyframe selection threshold (pixel) #imu parameters       The more accurate parameters you provide, the better performanceacc_n: 0.2          # accelerometer measurement noise standard deviation. #0.2   0.04gyr_n: 0.05        # gyroscope measurement noise standard deviation.     #0.05  0.004acc_w: 0.00004         # accelerometer bias random work noise standard deviation.  #0.02gyr_w: 4.0e-5       # gyroscope bias random work noise standard deviation.     #4.0e-5g_norm: 9.805    # gravity magnitude #loop closure parametersloop_closure: 1                    # start loop closurefast_relocalization: 1             # useful in real-time and large projectload_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'pose_graph_save_path: "~/output/pose_graph/" # save and load path #unsynchronization parametersestimate_td: 0                      # online estimate time offset between camera and imutd: 0.000                             # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) #rolling shutter parametersrolling_shutter: 0                  # 0: global shutter camera, 1: rolling shutter camerarolling_shutter_tr: 0               # unit: s. rolling shutter read out time per frame (from data sheet).  #visualization parameterssave_image: 1                   # save image in pose graph for visualization prupose; you can close this function by setting 0 visualize_imu_forward: 0        # output imu forward propogation to achieve low latency and high frequence resultsvisualize_camera_size: 0.4      # size of camera marker in RVIZ

之后打开终端，运行：

roslaunch realsense2_camera rs_t265.launch roslaunch vins_estimator realsense_fisheye.launch roslaunch vins_estimator vins_rviz.launch

最后

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