引言：为了提高yolo识别的质量，提高了yolo的版本，改用yolov8进行物体识别，同时系统兼容了低版本的yolo，包括基于C++的yolov3和yolov4，以及yolov7。
简介，为了提高识别速度，系统采用了GPU进行加速，在使用7W功率的情况，大概可以稳定在20FPS，满功率情况下可以适当提高。
硬件：D435摄像头，Jetson orin nano 8G
环境：ubuntu20.04，ros-noetic, yolov8
注：目标跟随是在木根识别的基础上进行，因此本小节和yolov8识别小节类似，只是在此基础上添加了跟随控制程序
步骤一： 启动摄像头，获取摄像头发布的图像话题
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roslaunch realsense2_camera rs_camera.launch 
没有出现红色报错，出现如下界面，表明摄像头启动成功
步骤二：启动yolov8识别节点
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roslaunch yolov8_ros yolo_v8.launch launch文件如下，参数use_cpu设置为false，因为实际使用GPU加速，不是CPU跑，另外参数pub_topic是yolov8识别到目标后发布出来的物体在镜头中的位置，程序作了修改，直接给出目标物的中心位置，其中参数image_topic是订阅的节点话题，一定要与摄像头发布的实际话题名称对应上。
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<?xml version="1.0" encoding="utf-8"?>
<launch>
<!-- Load Parameter -->
<param name="use_cpu" value="false" />
<!-- Start yolov8 and ros wrapper -->
<node pkg="yolov8_ros" type="yolo_v8.py" name="yolov8_ros" output="screen" >
<param name="weight_path" value="$(find yolov8_ros)/weights/yolov8n.pt"/>
<param name="image_topic" value="/camera/color/image_raw" />
<param name="pub_topic" value="/object_position" />
<param name="camera_frame" value="camera_color_frame"/>
<param name="visualize" value="false"/>
<param name="conf" value="0.3" />
</node>
</launch>
出现如下界面表示yolov8启动成功
步骤三：打开rqt工具，查看识别效果
注：步骤三不是必须的，可以跳过直接进行步骤四
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rqt_image_view 
等待出现如下界面后，选择yolov8/detection_image查看yolov8识别效果
步骤四：启动跟随控制程序
（1）、终端启动程序
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roslaunch follow_yolov8 follow_yolov8.launch 
（2）、launch文件详解
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<?xml version="1.0" encoding="utf-8"?>
<launch>
<param name="target_object_id" value="chair" />
<node pkg="follow_yolov8" type="follow_yolov8" name="follow_yolov8" output="screen" />
</launch>launch文件中加载的参数target_object_id是指定跟随的目标名称，无人机在识别到这个目标以后，会通过全向的速度控制保持目标始终在无人机的视野中。launch文件中指定参数chair，因此在识别chair以后，可以看到终端会打印日志已经识别到指定的目标物
步骤五：控制部分代码
此处抛砖引玉，仅仅做最简单的速度控制，读者可以根据自己的理解，添加类似PID等控制跟随的算法，本文不再展开
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#include <ros/ros.h>
#include <std_msgs/Bool.h>
#include <geometry_msgs/PoseStamped.h>
#include <geometry_msgs/TwistStamped.h>
#include <mavros_msgs/CommandBool.h>
#include <mavros_msgs/SetMode.h>
#include <mavros_msgs/State.h>
#include <mavros_msgs/PositionTarget.h>
#include <cmath>
#include <tf/transform_listener.h>
#include <nav_msgs/Odometry.h>
#include <mavros_msgs/CommandLong.h>
#include <yolov8_ros_msgs/BoundingBoxes.h>
#include <string>
#define MAX_ERROR 50
#define VEL_SET 0.15
#define ALTITUDE 0.40
using namespace std;
yolov8_ros_msgs::BoundingBoxes object_pos;
nav_msgs::Odometry local_pos;
mavros_msgs::State current_state;
mavros_msgs::PositionTarget setpoint_raw;
//检测到的物体坐标值
double position_detec_x = 0;
double position_detec_y = 0;
std::string Class = "no_object";
std::string target_object_id = "eight";
void state_cb(const mavros_msgs::State::ConstPtr& msg);
void local_pos_cb(const nav_msgs::Odometry::ConstPtr& msg);
void object_pos_cb(const yolov8_ros_msgs::BoundingBoxes::ConstPtr& msg);
int main(int argc, char **argv)
{
//防止中文输出乱码
setlocale(LC_ALL, "");
//初始化节点，名称为visual_throw
ros::init(argc, argv, "follow_yolov8");
//创建句柄
ros::NodeHandle nh;
//订阅无人机状态话题
ros::Subscriber state_sub = nh.subscribe<mavros_msgs::State>("mavros/state", 100, state_cb);
//订阅无人机实时位置信息
ros::Subscriber local_pos_sub = nh.subscribe<nav_msgs::Odometry>("/mavros/local_position/odom", 100, local_pos_cb);
//订阅实时位置信息
ros::Subscriber object_pos_sub = nh.subscribe<yolov8_ros_msgs::BoundingBoxes>("object_position", 100, object_pos_cb);
//发布无人机位置控制话题
ros::Publisher local_pos_pub = nh.advertise<geometry_msgs::PoseStamped>("mavros/setpoint_position/local", 100);
//发布无人机多维控制话题
ros::Publisher mavros_setpoint_pos_pub = nh.advertise<mavros_msgs::PositionTarget>("/mavros/setpoint_raw/local", 100);
//请求无人机解锁服务
ros::ServiceClient arming_client = nh.serviceClient<mavros_msgs::CommandBool>("mavros/cmd/arming");
//请求无人机设置飞行模式，本代码请求进入offboard
ros::ServiceClient set_mode_client = nh.serviceClient<mavros_msgs::SetMode>("mavros/set_mode");
//请求控制舵机客户端
ros::ServiceClient ctrl_pwm_client = nh.serviceClient<mavros_msgs::CommandLong>("mavros/cmd/command");
//循环频率
ros::Rate rate(20.0);
ros::param::get("target_object_id", target_object_id);
//等待连接到PX4无人机
while(ros::ok() && current_state.connected)
{
ros::spinOnce();
rate.sleep();
}
setpoint_raw.type_mask = 1 + 2 + /*4 + 8 + 16 + 32*/ + 64 + 128 + 256 + 512 + 1024 + 2048;
setpoint_raw.coordinate_frame = 8;
setpoint_raw.position.x = 0;
setpoint_raw.position.y = 0;
setpoint_raw.position.z = 0 + ALTITUDE;
mavros_setpoint_pos_pub.publish(setpoint_raw);
for(int i = 100; ros::ok() && i > 0; --i)
{
mavros_setpoint_pos_pub.publish(setpoint_raw);
ros::spinOnce();
rate.sleep();
}
//请求offboard模式变量
mavros_msgs::SetMode offb_set_mode;
offb_set_mode.request.custom_mode = "OFFBOARD";
//请求解锁变量
mavros_msgs::CommandBool arm_cmd;
arm_cmd.request.value = true;
ros::Time last_request = ros::Time::now();
//请求进入offboard模式并且解锁无人机，15秒后退出，防止重复请求
while(ros::ok())
{
//请求进入OFFBOARD模式
if( current_state.mode != "OFFBOARD" && (ros::Time::now() - last_request > ros::Duration(5.0)))
{
if( set_mode_client.call(offb_set_mode) && offb_set_mode.response.mode_sent)
{
ROS_INFO("Offboard enabled");
}
last_request = ros::Time::now();
}
else
{
//请求解锁
if( !current_state.armed && (ros::Time::now() - last_request > ros::Duration(5.0)))
{
if( arming_client.call(arm_cmd) && arm_cmd.response.success)
{
ROS_INFO("Vehicle armed");
}
last_request = ros::Time::now();
}
}
if(ros::Time::now() - last_request > ros::Duration(5.0))
break;
mavros_setpoint_pos_pub.publish(setpoint_raw);
ros::spinOnce();
rate.sleep();
}
while(ros::ok())
{
//此处表示识别到launch文件中指定的目标
//if(object_pos.bounding_boxes[0].Class == "chair")
if(Class == target_object_id)
{
ROS_INFO("识别到目标，采用速度控制进行跟随");
//摄像头向下安装，因此摄像头的Z对应无人机的X前后方向，Y对应Y左右方向
//无人机左右移动速度控制
if(position_detec_x-320 >= MAX_ERROR)
{
setpoint_raw.velocity.y = -VEL_SET;
}
else if(position_detec_x-320 <= -MAX_ERROR)
{
setpoint_raw.velocity.y = VEL_SET;
}
else
{
setpoint_raw.velocity.y = 0;
}
//无人机前后移动速度控制
if(position_detec_y-240 >= MAX_ERROR)
{
setpoint_raw.velocity.x = -VEL_SET;
}
else if(position_detec_y-240 <= -MAX_ERROR)
{
setpoint_raw.velocity.x = VEL_SET;
}
else
{
setpoint_raw.velocity.x = 0;
}
}
else
{
setpoint_raw.velocity.x = 0;
setpoint_raw.velocity.y = 0;
}
setpoint_raw.type_mask = 1 + 2 +/* 4 + 8 + 16 + 32*/ + 64 + 128 + 256 + 512 /*+ 1024 + 2048*/;
setpoint_raw.coordinate_frame = 8;
setpoint_raw.velocity.x = 0;
setpoint_raw.position.z = 0 + ALTITUDE;
setpoint_raw.yaw = 0;
mavros_setpoint_pos_pub.publish(setpoint_raw);
ros::spinOnce();
rate.sleep();
}
return 0;
}
void state_cb(const mavros_msgs::State::ConstPtr& msg)
{
current_state = *msg;
}
void local_pos_cb(const nav_msgs::Odometry::ConstPtr& msg)
{
local_pos = *msg;
}
void object_pos_cb(const yolov8_ros_msgs::BoundingBoxes::ConstPtr& msg)
{
object_pos = *msg;
position_detec_x = object_pos.bounding_boxes[0].xmin;
position_detec_y = object_pos.bounding_boxes[0].ymin;
Class = object_pos.bounding_boxes[0].Class;
}