双系统ubuntu20.04复现目标检测与跟踪过程，分享给大家：
1.环境配置
首先配置darknet_ros，它包含了Darknet 框架，YOLO算法以及ROS接口。
点击查看代码
cd ~/catkin_ws/src
git clone https://github.com/leggedrobotics/darknet_ros.git --recursive
catkin build darknet_ros --cmake-args -DCMAKE_BUILD_TYPE=Release
启动PX4室外场景仿真以及YOLO算法:
点击查看代码
# 启动yolo
roslaunch darknet_ros darknet_ros.launch
# 室外仿真
cd ~/PX4_Firmware
roslaunch px4 outdoor1.launch
但是YOLO此时并没有收到图像，并一直是 Waiting for image。执行 rostopic list 命令后发现并没有 typhoon_h480_0/cgo3_camera/image_raw 话题（来自typhoon_h480.sdf里的cgo3_camera_controller），只有darknet_ros默认订阅的/camera/rgb/image_raw,因此并不会弹出目标识别的窗口。
检查原因是gazebo_ros相机插件丢失，执行以下命令即可重新找回 typhoon_h480_0/cgo3_camera/image_raw 话题，并改~/catkin_ws/src/darknet_ros/darknet_ros/launch/darknet_ros.launch:
点击查看代码
sudo apt install ros-noetic-gazebo-ros-pkgs ros-noetic-gazebo-ros-control
# ~/catkin_ws/src/darknet_ros/darknet_ros/launch/darknet_ros.launch
# 将<arg name="image" default="/camera/rgb/image_raw" />改为
<arg name="image" default="/typhoon_h480_0/cgo3_camera/image_raw" />
在进行通讯时又碰到了无法定位的问题，解决思路和上一节相同。在~/XTDrone/sensing/slam/laser_slam/script文件夹下面新建laser_transfer_copy.py(注意里面包含了以前小节的内容，可以有取舍，另外大家碰到的问题可能不一致，最好从话题入手):
点击查看代码
import rospy
from geometry_msgs.msg import PoseStamped
from tf2_ros import TransformListener, Buffer
import sys
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from gazebo_msgs.msg import ModelStatesvehicle_type = sys.argv[1]
vehicle_id = sys.argv[2]
laser_slam_type = sys.argv[3]
rospy.init_node(vehicle_type+vehicle_id+'_'+laser_slam_type+'_laser_transfer')
pose_pub = rospy.Publisher(vehicle_type+'_'+ vehicle_id+"/mavros/vision_pose/pose", PoseStamped, queue_size=1)
tfBuffer = Buffer()
tflistener = TransformListener(tfBuffer)
local_pose = PoseStamped()
local_pose.header.frame_id = 'map'
hector = PoseStamped()
height = 0# 新增：用于 faster_lio 的回调
def odom_callback(odom_msg):pose_msg = PoseStamped()pose_msg.header = odom_msg.headerpose_msg.pose = odom_msg.pose.posepose_pub.publish(pose_msg)def hector_callback(data):global hectorhector = datadef height_distance_callback(msg):global heightheight = msg.ranges[0]if(height==float("inf")):height = 0def hector_slam():global local_pose, heightpose2d_sub = rospy.Subscriber(vehicle_type+'_'+ vehicle_id+"/pose", PoseStamped, hector_callback,queue_size=1)rate = rospy.Rate(100)while True:local_pose = hectorlocal_pose.pose.position.z = heightpose_pub.publish(local_pose)rate.sleep()def cartographer():global local_pose, heightrate = rospy.Rate(30)while not rospy.is_shutdown():try:tfstamped = tfBuffer.lookup_transform('map', 'base_link', rospy.Time(0))except:continuelocal_pose.header.stamp = rospy.Time().now()local_pose.pose.position = tfstamped.transform.translationlocal_pose.pose.position.z = heightlocal_pose.pose.orientation = tfstamped.transform.rotationpose_pub.publish(local_pose)rate.sleep()def aloam():global local_poserate = rospy.Rate(30)while not rospy.is_shutdown():try:tfstamped = tfBuffer.lookup_transform('camera_init', 'aft_mapped', rospy.Time(0))except:continuelocal_pose.header.stamp = rospy.Time().now()local_pose.pose.position = tfstamped.transform.translationlocal_pose.pose.orientation = tfstamped.transform.rotationpose_pub.publish(local_pose)rate.sleep()# 新增：faster_lio 转换函数（直接订阅 /Odometry）
def faster_lio():rospy.loginfo("Starting faster_lio converter, subscribing to /Odometry")rospy.Subscriber("/Odometry", Odometry, odom_callback)rospy.spin() # 保持节点运行# 新增：用于 Gazebo 真值（Ground Truth）的回调
def gazebo_gt_callback(model_states_msg):# 根据无人机名称提取位姿if vehicle_type+'_'+vehicle_id not in model_states_msg.name:returnidx = model_states_msg.name.index(vehicle_type+'_'+vehicle_id)pose_msg = PoseStamped()# 手动设置时间戳和坐标系pose_msg.header.stamp = rospy.Time.now()pose_msg.header.frame_id = "map" # 可以使用 "world" 或 "odom"pose_msg.pose = model_states_msg.pose[idx]pose_pub.publish(pose_msg)def publish_gazebo_gt():rospy.loginfo("Publishing Gazebo ground truth for %s_%s to vision_pose/pose", vehicle_type, vehicle_id)rospy.Subscriber("/gazebo/model_states", ModelStates, gazebo_gt_callback)rospy.spin()if __name__ == '__main__':if laser_slam_type == 'hector':height_distance_sub = rospy.Subscriber(vehicle_type+'_'+ vehicle_id+"/distance", LaserScan, height_distance_callback,queue_size=1)hector_slam()elif laser_slam_type == 'cartographer':height_distance_sub = rospy.Subscriber(vehicle_type+'_'+ vehicle_id+"/distance", LaserScan, height_distance_callback,queue_size=1)cartographer()elif laser_slam_type == 'aloam':aloam()elif laser_slam_type == 'faster_lio':faster_lio()elif laser_slam_type == 'ground_truth': # 新增分支publish_gazebo_gt()else:print('input error')
2.成功实现移动目标检测
重新执行下面代码：
点击查看代码
# 终端1
cd ~/PX4_Firmware
roslaunch px4 outdoor1.launch
# 终端2
roslaunch darknet_ros darknet_ros.launch
# 终端3
cd ~/XTDrone/sensing/slam/laser_slam/script
python3 laser_transfer_copy.py typhoon_h480 0 ground_truth
# 终端4
cd ~/XTDrone/communication
python3 multirotor_communication.py typhoon_h480 0
# 终端5
cd ~/XTDrone/control/keyboard
python3 multirotor_keyboard_control.py typhoon_h480 1 vel
由于我们使用的是轻量的YOLO算法，其检测精度一般（在终端2中可以看到其检测精度）。
