相机标定
作为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-setuptools
sudo apt-get install python-setuptools python-rosinstall ipython libeigen3-dev libboost-all-dev doxygen
sudo 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/src
git clone https://github.com/ethz-asl/Kalibr.git
cd ..
catkin_make
source ~/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：
target_type: 'aprilgrid' #gridtype
tagCols: 6 #number of apriltags
tagRows: 6 #number of apriltags
tagSize: 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 /fisheye2
rosrun 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
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 /fisheye2
rosrun 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
T265相机跑vins mono
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）
%YAML:1.0
#common parameters
imu_topic: "/camera/imu"
image_topic: "/camera/fisheye1/image_raw"
output_path: "~/output/"
#camera calibration
model_type: KANNALA_BRANDT
camera_name: camera
image_width: 848
image_height: 800
projection_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_cam
extrinsicRotation: !!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_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [0.0106988288462162, -0.000111902991193347, -0.000111670000478625]
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_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 topic
equalize: 0 # if image is too dark or light, trun on equalize to find enough features
fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points
#optimization parameters
max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time
max_num_iterations: 12 # max solver itrations, to guarantee real time
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
#imu parameters The more accurate parameters you provide, the better performance
acc_n: 0.2 # accelerometer measurement noise standard deviation. #0.2 0.04
gyr_n: 0.05 # gyroscope measurement noise standard deviation. #0.05 0.004
acc_w: 0.00004 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 4.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
fast_relocalization: 1 # useful in real-time and large project
load_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 parameters
estimate_td: 0 # online estimate time offset between camera and imu
td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#rolling shutter parameters
rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0 # unit: s. rolling shutter read out time per frame (from data sheet).
#visualization parameters
save_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 results
visualize_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
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