/mavros/setpoint_position

/mavros/setpoint_position是ROS中的一个话题(topic)，它用于向飞控发送目标位置信息，以实现无人机的位置控制。具体来说，该话题包含机器人在本地坐标系下的目标坐标和姿态信息。通过向该话题发布目标位置信息，可以控制无人机朝着目标位置飞行，并保持目标姿态。该话题通常用于无人机的自主飞行和避障等应用。

相关问题

根据[WARN] [1686536287.864845, 877.306000]: Could not process inbound connection: topic types do not match: [geometry_msgs/PoseStamped] vs. [quadrotor_msgs/PositionCommand]{'callerid': '/mavros', 'md5sum': 'd3812c3cbc69362b77dc0b19b345f8f5', 'tcp_nodelay': '0', 'topic': '/mavros/setpoint_position/local', 'type': 'geometry_msgs/PoseStamped'}修改local_pos_pub3 = rospy.Publisher('mavros/setpoint_position/local', PositionCommand, queue_size=10)

根据错误提示，这个问题是由于订阅者期望接收的消息类型与实际发布者发布的消息类型不匹配导致的。可以将发布者的消息类型改为与订阅者期望接收的类型相同，或者将订阅者的消息类型修改为与发布者发布的类型相同。

在这个例子中，可以将代码修改为：

local_pos_pub3 = rospy.Publisher('mavros/setpoint_position/local', PoseStamped, queue_size=10)

这样就将发布的消息类型改为了 `PoseStamped`，与订阅者期望接收的类型相同，从而避免了消息类型不匹配的问题。

kcf无人机跟踪小车ros代码,要求订阅/iris/usb_cam/image_raw话题，发布mavros/setpoint_position/local和/mavros/setpoint_velocity/cmd_vel_unstamped

以下是一个简单的ROS节点示例，用于使用KCF算法跟踪小车并发布位置和速度指令到MAVROS。

#!/usr/bin/env python

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import numpy as np
from pyimagesearch.centroidtracker import CentroidTracker
from pyimagesearch.trackableobject import TrackableObject
from geometry_msgs.msg import PoseStamped, Twist

class KCFTrackerNode:
    def __init__(self):
        rospy.init_node('kcf_tracker_node', anonymous=True)

        self.bridge = CvBridge()
        self.ct = CentroidTracker()
        self.trackers = []
        self.trackable_objects = {}

        self.image_sub = rospy.Subscriber('/iris/usb_cam/image_raw', Image, self.image_callback)
        self.position_pub = rospy.Publisher('/mavros/setpoint_position/local', PoseStamped, queue_size=1)
        self.velocity_pub = rospy.Publisher('/mavros/setpoint_velocity/cmd_vel_unstamped', Twist, queue_size=1)

        self.image_width = 640
        self.image_height = 480
        self.focal_length = 600
        self.real_width = 0.5
        self.target_width = 0.1

    def image_callback(self, data):
        cv_image = self.bridge.imgmsg_to_cv2(data, 'bgr8')

        if len(self.trackers) == 0:
            # initialize trackers
            objects = self.ct.update([(0, 0, self.image_width, self.image_height)])
            for (object_id, centroid) in objects.items():
                tracker = cv2.TrackerKCF_create()
                tracker.init(cv_image, (centroid[0], centroid[1], self.target_width * self.focal_length, self.target_width * self.focal_length))
                self.trackers.append(tracker)
                self.trackable_objects[object_id] = TrackableObject(object_id, centroid)

        else:
            # update trackers
            for tracker in self.trackers:
                success, box = tracker.update(cv_image)
                if success:
                    (x, y, w, h) = [int(v) for v in box]
                    centroid = (x + w / 2, y + h / 2)
                    object_id = self.ct.register(centroid)
                    to = self.trackable_objects.get(object_id, None)
                    if to is None:
                        to = TrackableObject(object_id, centroid)
                        self.trackable_objects[object_id] = to
                    else:
                        to.centroids.append(centroid)

        # filter out small objects
        self.trackable_objects = {k: v for k, v in self.trackable_objects.items() if len(v.centroids) > 5 and v.width() > self.image_width * 0.1}

        # update position and velocity commands
        for object_id, to in self.trackable_objects.items():
            x = (to.centroids[-1][0] - self.image_width / 2) * self.real_width / self.focal_length
            y = (to.centroids[-1][1] - self.image_height / 2) * self.real_width / self.focal_length
            z = 2.0

            pose_msg = PoseStamped()
            pose_msg.header.stamp = rospy.Time.now()
            pose_msg.pose.position.x = x
            pose_msg.pose.position.y = y
            pose_msg.pose.position.z = z
            self.position_pub.publish(pose_msg)

            vx = (to.centroids[-1][0] - to.centroids[-2][0]) * self.real_width / self.focal_length
            vy = (to.centroids[-1][1] - to.centroids[-2][1]) * self.real_width / self.focal_length
            vz = 0.0

            vel_msg = Twist()
            vel_msg.linear.x = vx
            vel_msg.linear.y = vy
            vel_msg.linear.z = vz
            self.velocity_pub.publish(vel_msg)

if __name__ == '__main__':
    try:
        node = KCFTrackerNode()
        rospy.spin()
    except rospy.ROSInterruptException:
        pass

请注意，此节点使用了pyimagesearch库中的CentroidTracker和TrackableObject类，您需要先安装此库：

pip install imutils

此外，这里的代码将图像中间作为目标点，将图像宽度的10％用作最小目标宽度，将真实世界中的实际宽度设置为0.5米。您可能需要根据您的具体应用程序进行一些修改。