[转]ROS 传感器消息及RVIZ可视化Laserscan和PointCloud

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https://blog.csdn.net/yangziluomu/article/details/79576508

https://answers.ros.org/question/60239/how-to-extract-and-use-laser-data-from-scan-topic-in-ros-using-c/

https://answers.ros.org/question/149256/subscribe-laserscan/

ROS 传感器消息

在使用ROS各个传感器消息之前，弄清楚各个传感器在ROS是如何表示的显得极为重要。特别是，Laserscan, PointCloud等用了很久之后，感觉已经很熟悉了，但是一些细节行的东西一直没有深究，并且对某些参数难以形成直觉上的认识，很大程度上影响了在与其他算法或者硬件衔接时的问题。这里，我单独的将Laserscan，PointCloud发布在rviz中，通过修改不同的ROS消息参数，直观的观察其在参数的作用。整个工程代码可以在此处下载：https://download.csdn.net/download/ziqian0512/10289936。

ROS 传感器消息之Laserscan

消息定义

首先，查看一下sensor_msgs/LaserScan.msg的定义，见ros.org/LaserScan.“`

Header header float32 angle_min # start angle of the scan [rad] float32 angle_max # end angle of the scan [rad] float32 angle_increment # angular distance between measurements [rad] float32 time_increment # time between measurements [seconds] float32 scan_time # time between scans [seconds] float32 range_min # minimum range value [m] float32 range_max # maximum range value [m] float32[] ranges # range data [m] (Note: values < range_min or > range_max should be discarded) float32[] intensities # intensity data [device-specific units]

在消息中需要特别说明的主要是ranges和intensities，ranges表示在某一个角度时，扫描到物体离自己的距离，这很容易理解。intensities则表示测量的强度，也就是激光所反射的亮度，通常来说，值越大，光反射越亮。也就可以用intensities做一些假设，推荐扫描到物体的材料。比如，Hokyuo Laser scanner就提供了反射的所有强度，而 Sick S300则只检测是否由反光带。

测试代码

以下为测试Laserscan源码，需要特别关注的num_readings（点数），ranges（距离），intensities（强度）参数在调节过程中，rviz中Laserscan的变化。
编译以下代码后，还需要选择sensor_frame坐标系，和消息/scan。效果图如下：

代码：

#include <ros/ros.h> #include <sensor_msgs/LaserScan.h>int main(int argc, char** argv){ ros::init(argc, argv, "laser_scan_publisher"); ros::NodeHandle n; ros::Publisher scan_pub = n.advertise<sensor_msgs::LaserScan>("scan", 50); unsigned int num_readings = 100; double laser_frequency = 40; double ranges[num_readings]; double intensities[num_readings]; int count = 10; ros::Rate r(1.0); while(n.ok()){ //generate some fake data for our laser scan for(unsigned int i = 0; i < num_readings; ++i){ ranges[i] = count; intensities[i] = 10*i; } ros::Time scan_time = ros::Time::now(); //populate the LaserScan message sensor_msgs::LaserScan scan; scan.header.stamp = scan_time; scan.header.frame_id = "sensor_frame"; scan.angle_min = -1.57; scan.angle_max = 1.57; scan.angle_increment = 3.14 / num_readings; scan.time_increment = (1 / laser_frequency) / (num_readings); scan.range_min = 0.0; scan.range_max = 100.0; scan.ranges.resize(num_readings); scan.intensities.resize(num_readings); for(unsigned int i = 0; i < num_readings; ++i){ scan.ranges[i] = ranges[i]; scan.intensities[i] = intensities[i]; } scan_pub.publish(scan); // ++count; ROS_WARN_STREAM("count"<<count); r.sleep(); } }

ROS 传感器消息之PointCloud

消息定义

PointCloud 描述如下：

Header header geometry_msgs/Point32[] points # Array of 3d points ChannelFloat32[] channels # channel name, value

channels可以进一步解释为：

# "rgb" - For point clouds produced by color stereo cameras. uint8 (R,G,B) values packed into the least significant 24 bits,in order. # "intensity" - laser or pixel intensity.# The channel name should give the semantics of the channel (e.g. "intensity" instead of "value"). string name# The values array should be 1-1 with the elements of the associated PointCloud. float32[] values

name表示不同的表示方式，可以是像素值，亮度等。values则是典型的依赖于传感器的亮度值返回，表示测量的质量，可以是灰度图像的灰度值，也可以是类似laserscan的强度。

测试代码

在以下代码中，重点需要调节num_points（点数），channels（设置通道，以及强度），points（位置）参数来观察PointCloud的变化。编译以下代码后，还需要选择sensor_frame坐标系，和消息/cloud。效果图如下：

代码

#include <ros/ros.h> #include <sensor_msgs/PointCloud.h>int main(int argc, char** argv){ ros::init(argc, argv, "point_cloud_publisher"); ros::NodeHandle n; ros::Publisher cloud_pub = n.advertise<sensor_msgs::PointCloud>("/cloud", 50); unsigned int num_points = 2000; int count = 0; ros::Rate r(1.0); while(n.ok()){ sensor_msgs::PointCloud cloud; cloud.header.stamp = ros::Time::now(); cloud.header.frame_id = "sensor_frame"; cloud.points.resize(num_points); //we'll also add an intensity channel to the cloud cloud.channels.resize(1); cloud.channels[0].name = "intensities"; cloud.channels[0].values.resize(num_points); //generate some fake data for our point cloud for(unsigned int i = 0; i < num_points; ++i){ cloud.points[i].x = 1 + i/100; cloud.points[i].y = 1; cloud.points[i].z = 1; cloud.channels[0].values[i] = 100 * i; } cloud_pub.publish(cloud); // ++count; r.sleep(); } }