VINS-Mono代码详解 ——— （1）启动文件euroc.launch + 参数配置文件euroc_config.yaml

一、启动文件euroc.launch

启动文件euroc.launch：为了一次运行多个node。

1. 文件位置

2. 文件内容

2.1 launch文件语法

• pkg=package名字(在package.xml定义)
  
  

• type=可执行文件名字(在CMakeLists.txt定义）
  

2.2 设置局部变量"config_path"，表示euroc_config.yaml的具体地址；

<arg name="config_path" default = "$(find feature_tracker)/../config/euroc/euroc_config.yaml" /><arg name="vins_path" default = "$(find feature_tracker)/../config/../" />

设置局部变量"vins_path"，在parameters.cpp中使用鱼眼相机mask中用到：

std::string VINS_FOLDER_PATH = readParam<std::string>(n, "vins_folder");
if (FISHEYE == 1)FISHEYE_MASK = VINS_FOLDER_PATH + "config/fisheye_mask.jpg";

2.3 启动"feature_tracker"节点，在该节点中需要读取参数文件，地址为config_file，即config_path；

<node name="feature_tracker" pkg="feature_tracker" type="feature_tracker" output="log"><param name="config_file" type="string" value="$(arg config_path)" /><param name="vins_folder" type="string" value="$(arg vins_path)" />
</node>

2.4 启动"vins_estimator"节点，内容同上；

<node name="vins_estimator" pkg="vins_estimator" type="vins_estimator" output="screen"><param name="config_file" type="string" value="$(arg config_path)" /><param name="vins_folder" type="string" value="$(arg vins_path)" />
</node>

2.5 启动"pose_graph"节点，除了参数配置文件的地址外还设置了4个参数：

<node name="pose_graph" pkg="pose_graph" type="pose_graph" output="screen"><param name="config_file" type="string" value="$(arg config_path)" /><param name="visualization_shift_x" type="int" value="0" /><param name="visualization_shift_y" type="int" value="0" /><param name="skip_cnt" type="int" value="0" /><param name="skip_dis" type="double" value="0" />
</node>

visualization_shift_x和visualization_shift_y表示在进行位姿图优化后，对得到的位姿在x坐标和y坐标的偏移量（一般都设为0）；

geometry_msgs::PoseStamped pose_stamped;
pose_stamped.header.stamp = ros::Time(cur_kf->time_stamp);
pose_stamped.header.frame_id = "world";
pose_stamped.pose.position.x = P.x() + VISUALIZATION_SHIFT_X;
pose_stamped.pose.position.y = P.y() + VISUALIZATION_SHIFT_Y;
pose_stamped.pose.position.z = P.z();

skip_cnt在pose_graph_node的process()中，表示每隔skip_cnt个图像帧才进行一次处理；
skip_dis也在pose_graph_node的process()中，目的是将距上一帧的时间间隔超过SKIP_DIS的图像创建为位姿图中的关键帧。

二、参数配置文件euroc_config.yaml

1. 文件位置

2. 文件内容

%YAML:1.0#common parameters
imu_topic: "/imu0"                          
image_topic: "/cam0/image_raw"
output_path: "/home/shaozu/output/"#camera calibration 
model_type: PINHOLE
camera_name: camera
image_width: 752
image_height: 480
distortion_parameters:k1: -2.917e-01k2: 8.228e-02p1: 5.333e-05p2: -1.578e-04
projection_parameters:fx: 4.616e+02fy: 4.603e+02cx: 3.630e+02cy: 2.481e+02# Extrinsic parameter between IMU and Camera.
#IMU与摄像机之间的外部参数。
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# 0 有一个精确的外部参数。 我们会信任下面的imu和r_cam，imu和t_cam，不要改变它。# 1 对外部参数有初步的猜测。 我们将围绕您的初步猜测进行优化。# 2 对外界参数一无所知。 你不需要给R，T。 我们会试着校准的。 开始时做一些旋转动作。
# 如果你选择0或1，你应该写下下面的矩阵。
# 从相机架旋转到imu架、imu^r_cam
extrinsicRotation: !!opencv-matrixrows: 3cols: 3dt: ddata: [0.0148655429818, -0.999880929698, 0.00414029679422,0.999557249008, 0.0149672133247, 0.025715529948, -0.0257744366974, 0.00375618835797, 0.999660727178]
#Translation from camera frame to imu frame, imu^T_cam
#从相机到imu的转换，imu^T_cam
extrinsicTranslation: !!opencv-matrixrows: 3cols: 1dt: ddata: [-0.0216401454975,-0.064676986768, 0.00981073058949]#feature traker paprameters
#特征跟踪参数
max_cnt: 150            # max feature number in feature tracking# 进行特征光流跟踪时保持的最大特征点数量
min_dist: 30            # min distance between two features # 两个相邻特征之间像素的最小间隔
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 # (赫兹) 发布跟踪结果。 至少10Hz，以获得良好的估计。 如果设置为0，频率将与原始图像相同
F_threshold: 1.0        # ransac threshold (pixel)# ransac阈值 (像素)
show_track: 1           # publish tracking image as topic# 发布跟踪图像作为主题
equalize: 1             # if image is too dark or light, trun on equalize to find enough features# 如果图像太暗或太亮，按直方图均衡化查找足够的特征
fisheye: 0              # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points# 如果用鱼眼，就转动它。 将加载一个圆形掩码以消除边缘噪声点# 鱼眼相机一般需要圆形的mask，以去除外部噪声点。mask图在config文件夹中。#optimization parameters
#优化参数
max_solver_time: 0.04  # max solver itration time (ms), to guarantee real time# ceres优化器的最大迭代时间，以保证实时性。
max_num_iterations: 8   # max solver itrations, to guarantee real time# ceres优化器的最大迭代次数，以保证实时性。
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)# 关键帧选择阈值 (像素)
#imu parameters The more accurate parameters you provide, the better performance
#imu参数 您提供的参数越准确，性能越好
acc_n: 0.08          # accelerometer measurement noise standard deviation. #0.2   0.04# 加速度传感器测量噪声标准差
gyr_n: 0.004         # gyroscope measurement noise standard deviation.     #0.05  0.004# 陀螺仪测量噪声标准差
acc_w: 0.00004         # accelerometer bias random work noise standard deviation.  #0.02# 加速度计偏置随机噪声标准差
gyr_w: 2.0e-6       # gyroscope bias random work noise standard deviation.     #4.0e-5# 随机噪声标准偏差
g_norm: 9.81007     # gravity magnitude# 重力大小#loop closure parameters
#闭环参数
loop_closure: 1                    # start loop closure# 使用闭环
load_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'# 加载并重用前一个pose图；pose_graph_save_pathe的负载
fast_relocalization: 0             # useful in real-time and large project# 可用于实时大型项目
pose_graph_save_path: "/home/shaozu/output/pose_graph/" # save and load path# 储存及载入路径#unsynchronization parameters
#在线时差校准
estimate_td: 0                      # online estimate time offset between camera and imu# 摄像机与imu之间的在线估计时间偏移
td: 0.0                             # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)# 时间偏移的初始值。 单位：s。readed图像时钟+td=real image时钟 (IMU时钟)#rolling shutter parameters
# 支持卷帘相机
rolling_shutter: 0                  # 0: global shutter camera, 1: rolling shutter camera# 0表示全局曝光相机；设置为1表示卷帘曝光相机
rolling_shutter_tr: 0               # unit: s. rolling shutter read out time per frame (from data sheet). # 单位：s  卷动快门读出时间 (来自数据表)
#visualization parameters
# 可视化参数
save_image: 1                   # save image in pose graph for visualization prupose; you can close this 
function by setting 0 # 将图像保存在体位图中，实现可视化保存；您可以通过设置0关闭此函数
visualize_imu_forward: 0        # output imu forward propogation to achieve low latency and high frequence results# 输出imu前馈实现低延迟、高频率的效果
visualize_camera_size: 0.4      # size of camera marker in RVIZ# 摄像机标记在RVIZ中的尺寸

三、参考文献

CSDN博主「Manii」的文章