rosparam 参数设置

写在前面

launch一个文件时，roslaunch首先检查roscore是否已经启动，如果没有则启动roscore。
roscore会做三件事：

• 启动master节点，该节点是隐藏的，用于通过消息名查询目标节点，实现消息、服务在各个节点之间的连接
• 启动parameter server，用于设置与查询参数
• 启动日志节点，记录所有消息收发和stdout、stderr,

rosparam 设置与访问方式

parameter server参数服务器，可以方便地通过设置参数来改变节点的行为。参数服务器内的参数是以key，value的形式交互。 其中key可以加前缀作为命名空间构成多级参数。 进行rosparam 设置与访问方式主要有如下三种方式：

• ros调试命令 rosparam set / rosparam get
• launch param /rosparam元素
• C++/Python API
  – roscpp: ros::param::set / ros::param::get
  –rospy: set_param / get_param

通常方法2和方法3是联系在一起的，通过2定义程序中经常需要调整的量，然后在程序中使用3进行获取。下面着重讲一下方法2与3

• 简单(少量)参数设置与调用
  仅仅使用param 设置几个参数，并包含其默认值
• 复杂(大量)参数设置与调用
  先通过一个param加载yaml文件，然后通过yaml文件来加载大量参数

使用示例

• launch文件 设置参数
  其中vins_folder 是简单参数设置，config_file是复杂参数设置

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• C++ API
  – getParam 获取单个参数
  – 通过getParam获取yaml文件名后，再通过fsSettings 读取yaml内容

#include 
#include 

std::string IMAGE_TOPIC;
std::string IMU_TOPIC;
std::vector CAM_NAMES;
std::string FISHEYE_MASK;
int MAX_CNT;
int MIN_DIST;
int WINDOW_SIZE;
int FREQ;
double F_THRESHOLD;
int SHOW_TRACK;
int STEREO_TRACK;
int EQUALIZE;
int ROW;
int COL;
int FOCAL_LENGTH;
int FISHEYE;
bool PUB_THIS_FRAME;

template 
T readParam(ros::NodeHandle &n, std::string name)
{
    T ans;
    if (n.getParam(name, ans)) 
    {
        ROS_INFO_STREAM("Loaded " << name << ": " << ans);
    }
    else
    {
        ROS_ERROR_STREAM("Failed to load " << name);
        n.shutdown();
    }
    return ans;
}

void readParameters(ros::NodeHandle &n)
{
    std::string config_file;
    config_file = readParam(n, "config_file");
    cv::FileStorage fsSettings(config_file, cv::FileStorage::READ);
    if(!fsSettings.isOpened())
    {
        std::cerr << "ERROR: Wrong path to settings" << std::endl;
    }
    std::string VINS_FOLDER_PATH = readParam(n, "vins_folder"); //读取单个参数

	//读取yaml
    fsSettings["image_topic"] >> IMAGE_TOPIC;
    fsSettings["imu_topic"] >> IMU_TOPIC;
    MAX_CNT = fsSettings["max_cnt"];
    MIN_DIST = fsSettings["min_dist"];
    ROW = fsSettings["image_height"];
    COL = fsSettings["image_width"];
    FREQ = fsSettings["freq"];
    F_THRESHOLD = fsSettings["F_threshold"];
    SHOW_TRACK = fsSettings["show_track"];
    EQUALIZE = fsSettings["equalize"];
    FISHEYE = fsSettings["fisheye"];
    if (FISHEYE == 1)
        FISHEYE_MASK = VINS_FOLDER_PATH + "config/fisheye_mask.jpg";
    CAM_NAMES.push_back(config_file);

    WINDOW_SIZE = 20;
    STEREO_TRACK = false;
    FOCAL_LENGTH = 460;
    PUB_THIS_FRAME = false;

    if (FREQ == 0)
        FREQ = 100;

    fsSettings.release();


}

• yaml文件如下

%YAML:1.0

#common parameters
imu_topic: "/imu0"
image_topic: "/cam0/image_raw"
output_path: "/home/hualong/Documents/vins_output"

#camera calibration 
model_type: PINHOLE
camera_name: camera
image_width: 752
image_height: 480
distortion_parameters:
   k1: -2.917e-01
   k2: 8.228e-02
   p1: 5.333e-05
   p2: -1.578e-04
projection_parameters:
   fx: 4.616e+02
   fy: 4.603e+02
   cx: 3.630e+02
   cy: 2.481e+02

# 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: [0.0148655429818, -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
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [-0.0216401454975,-0.064676986768, 0.00981073058949]

#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: 1             # 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: 8   # 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: 0.08          # accelerometer measurement noise standard deviation. #0.2   0.04
gyr_n: 0.004         # gyroscope measurement noise standard deviation.     #0.05  0.004
acc_w: 0.00004         # accelerometer bias random work noise standard deviation.  #0.02
gyr_w: 2.0e-6       # gyroscope bias random work noise standard deviation.     #4.0e-5
g_norm: 9.81007     # gravity magnitude

#loop closure parameters
loop_closure: 1                    # start loop closure
load_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'
fast_relocalization: 0             # useful in real-time and large project
pose_graph_save_path: "/home/tony-ws1/output/pose_graph/" # save and load path

#unsynchronization parameters
estimate_td: 0                      # online estimate time offset between camera and imu
td: 0.0                             # 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