ros:map_sever

本文深入解析了ROS中的Map_Server节点,介绍了如何通过该节点提供地图数据服务,以及使用map_saver工具保存动态生成的地图到文件的过程。文章详细阐述了地图格式、YAML配置参数、颜色值解释,并提供了命令行工具的使用示例。

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map_server provides the map_server ROS Node, which offers map data as a ROS Service. It also provides the map_saver command-line utility, which allows dynamically generated maps to be saved to file.

Map format
Maps manipulated by the tools in this package are stored in a pair of files. The YAML file describes the map meta-data, and names the image file. The image file encodes the occupancy data.

YAML format
The YAML format is best explained with a simple, complete example:

image: testmap.png
resolution: 0.1
origin: [0.0, 0.0, 0.0]
occupied_thresh: 0.65
free_thresh: 0.196
negate: 0
Required fields:

image : Path to the image file containing the occupancy data; can be absolute, or relative to the location of the YAML file

resolution : Resolution of the map, meters / pixel

origin : The 2-D pose of the lower-left pixel in the map, as (x, y, yaw), with yaw as counterclockwise rotation (yaw=0 means no rotation). Many parts of the system currently ignore yaw.

occupied_thresh : Pixels with occupancy probability greater than this threshold are considered completely occupied.

free_thresh : Pixels with occupancy probability less than this threshold are considered completely free.

negate : Whether the white/black free/occupied semantics should be reversed (interpretation of thresholds is unaffected)

Optional parameter:

mode : Can have one of three values: trinary, scale, or raw. Trinary is the default. More information on how this changes the value interpretation is in the next section.

Value Interpretation
Given a pixel that has a COLOR value x in the range [0, 256), how should we interpret this value when put into the ROS message? First we convert integer x to a floating point number p depending on the interpretation of the negate flag from the yaml.

If negate is false, p = (255 - x) / 255.0. This means that black (0) now has the highest value (1.0) and white (255) has the lowest (0.0).

If negate is true, p = x / 255.0. This is the non-standard interpretation of images, which is why it is called negate, even though the math indicates that x is not negated. Nomenclature is hard.

Trinary
The standard interpretation is the trinary interpretation, i.e. interpret all values so that the output ends up being one of three values.

If p > occupied_thresh, output the value 100 to indicate the cell is occupied.
If p < free_thresh, output the value 0 to indicate the cell is free.
Otherwise, output -1 a.k.a. 255 (as an unsigned char), to indicate that the cell is unknown.

Scale
This tweaks the above interpretation to allow for more output values than trinary.

As before if p > occupied_thresh, output the value 100. If p < free_thresh, output the value 0.

Otherwise, output 99 * (p - free_thresh) / (occupied_thresh - free_thresh)

This will allow you to output a full gradient of values ranging from [0, 100]. To output -1, simply use the alpha channel of a png, where any transparency will be interpreted as unknown.

Raw
This mode will output x for each pixel, so output values are [0, 255].

Command-line Tools

map_server
map_server is a ROS node that reads a map from disk and offers it via a ROS service.
The current implementation of the map_server converts color values in the map image data into ternary occupancy values: free (0), occupied (100), and unknown (-1). Future versions of this tool may use the values between 0 and 100 to communicate finer gradations of occupancy.

Usage

map_server <map.yaml>
Example

rosrun map_server map_server mymap.yaml
Note that the map data may be retrieved via either latched topic (meaning that it is sent once to each new subscriber), or via service. The service may eventually be phased out.

Published Topics
map_metadata (nav_msgs/MapMetaData)
Receive the map metadata via this latched topic.
map (nav_msgs/OccupancyGrid)
Receive the map via this latched topic.
Services
static_map (nav_msgs/GetMap)
Retrieve the map via this service.
Parameters
~frame_id (string, default: “map”)
The frame to set in the header of the published map.

map_saver
map_saver saves a map to disk, e.g., from a SLAM mapping service.
Usage

rosrun map_server map_saver [-f mapname]
map_saver retrieves map data and writes it out to map.pgm and map.yaml. Use the -f option to provide a different base name for the output files.

Example

rosrun map_server map_saver -f mymap

Subscribed Topics
map (nav_msgs/OccupancyGrid)
Map will be retrieved via this latched topic.

简单地说,map_server 提供了获取地图数据和保存地图数据的方法;获取地图数据通过map_server节点实现,保存数据通过map_saver节点订阅/map话题实现。

当运行出现下面错误时,可以通过rqt_graph看节点之间是否连通,需要注意的是,map_saver订阅的是/map 话题;
rosrun map_server map_saver -f mymap
[ INFO] [1541980723.161416738]: Waiting for the map

#include <ros/ros.h> #include <nav_msgs/Odometry.h> #include <geometry_msgs/PoseStamped.h> #include <tf2_ros/transform_broadcaster.h> #include <tf2_geometry_msgs/tf2_geometry_msgs.h> class OdometryProcessor { public: OdometryProcessor() : nh_("~") { // 初始化发布器和订阅器 odom_pub_ = nh_.advertise<nav_msgs::Odometry>("odom", 10); amcl_sub_ = nh_.subscribe("amcl_pose", 1, &OdometryProcessor::amclCallback, this); // 初始化TF广播器 tf_broadcaster_ = std::make_unique<tf2_ros::TransformBroadcaster>(); } void amclCallback(const geometry_msgs::PoseWithCovarianceStampedConstPtr& msg) { // 接收AMCL定位数据 current_amcl_pose_ = msg->pose.pose; last_amcl_time_ = ros::Time::now(); } void publishOdom() { // 创建Odometry消息 nav_msgs::Odometry odom_msg; odom_msg.header.stamp = ros::Time::now(); odom_msg.header.frame_id = "odom"; odom_msg.child_frame_id = "base_link"; // 填充位姿和速度数据(此处需根据实际传感器数据填充) odom_msg.pose.pose.position.x = 0.0; // 实际应用中替换为真实值 odom_msg.pose.pose.orientation.w = 1.0; odom_msg.twist.twist.linear.x = 0.1; // 示例速度值 // 发布Odometry消息 odom_pub_.publish(odom_msg); // 发布odom->base_link的TF变换 publishOdomTransform(odom_msg); // 处理AMCL数据(如果已接收) if ((ros::Time::now() - last_amcl_time_).toSec() < 1.0) { processAmclData(odom_msg); } } private: void publishOdomTransform(const nav_msgs::Odometry& odom) { geometry_msgs::TransformStamped transform; transform.header = odom.header; transform.child_frame_id = odom.child_frame_id; transform.transform.translation.x = odom.pose.pose.position.x; transform.transform.translation.y = odom.pose.pose.position.y; transform.transform.translation.z = odom.pose.pose.position.z; transform.transform.rotation = odom.pose.pose.orientation; tf_broadcaster_->sendTransform(transform); } void processAmclData(const nav_msgs::Odometry& odom) { // 计算map->odom变换 geometry_msgs::TransformStamped map_to_odom; map_to_odom.header.stamp = ros::Time::now(); map_to_odom.header.frame_id = "map"; map_to_odom.child_frame_id = "odom"; // 计算变换关系:map->odom = map->base_link * base_link->odom tf2::Transform map_to_base, base_to_odom; tf2::fromMsg(current_amcl_pose_, map_to_base); tf2::fromMsg(odom.pose.pose, base_to_odom); // 计算逆变换:base_link->odom的逆是odom->base_link tf2::Transform odom_to_base = base_to_odom.inverse(); tf2::Transform map_to_odom_tf = map_to_base * odom_to_base; map_to_odom.transform = tf2::toMsg(map_to_odom_tf); tf_broadcaster_->sendTransform(map_to_odom); } ros::NodeHandle nh_; ros::Publisher odom_pub_; ros::Subscriber amcl_sub_; std::unique_ptr<tf2_ros::TransformBroadcaster> tf_broadcaster_; geometry_msgs::Pose current_amcl_pose_; ros::Time last_amcl_time_; }; int main(int argc, char** argv) { ros::init(argc, argv, "odometry_node"); OdometryProcessor processor; ros::Rate rate(20); // 20Hz while (ros::ok()) { ros::spinOnce(); processor.publishOdom(); rate.sleep(); } return 0; } 基于该代码完成发布odmo话题并接收acml模块的odmo数据并转换成base_like
最新发布
06-21
<think>我们有一个ROS节点,用于处理里程计和AMCL定位数据。主要功能包括:1.订阅AMCL的定位结果(geometry_msgs/PoseWithCovarianceStamped),用于获取map到base_link的变换。2.发布Odometry消息(nav_msgs/Odometry)到"odom"话题,并发布odom到base_link的TF变换。3.利用AMCL的数据计算map到odom的变换并发布(以修正里程计的漂移)。但是,代码中有一个问题:在`publishOdom`函数中,我们发布的Odometry消息的位姿数据是固定的(0.0和1.0),这显然不是实际数据。我们需要根据实际情况来填充这些数据。另外,代码中提到了“接收acml模块的odmo数据并转换成base_like”,但我们的回调函数是接收`amcl_pose`(AMCL的定位结果),而AMCL的输出是map到base_link的位姿。我们需要做的是:1.通常,我们会有一个里程计源(如轮式编码器、IMU等)提供odom到base_link的变换。这个里程计源应该发布Odometry消息,或者我们在这个节点中通过订阅原始里程计数据来获取。2.在这个节点中,我们并没有看到订阅原始里程计的代码。因此,我们需要修改代码,使其能够接收原始里程计数据(例如,可能是来自机器人底盘的Odometry消息)。根据问题描述,我们需要发布odom话题并接收acml模块的odmo数据(这里可能是笔误,应该是amcl数据)并转换成base_link。所以,我们可能需要订阅两个话题:-原始的里程计数据(用于获取odom->base_link的变换)-AMCL的数据(用于获取map->base_link的变换)然后,我们利用这两个数据计算map->odom的变换(即修正odom坐标系的原点,使得base_link在map中的位姿与AMCL的定位一致)。修正步骤:-假设我们有一个里程计源,它提供了odom->base_link的变换(通过Odometry消息,其child_frame_id为base_link,header.frame_id为odom)。-同时,AMCL提供了map->base_link的变换(通过PoseWithCovarianceStamped消息,其header.frame_id为map,而位姿是相对于map的base_link)。-那么,map->odom的变换可以通过以下方式计算:map->odom=map->base_link*(odom->base_link)^{-1}因此,我们需要修改代码:1.添加一个订阅器,用于订阅原始的Odometry消息(例如,话题名为"raw_odom")。2.在原始里程计的回调函数中,保存最新的里程计数据(即odom->base_link的变换)。3.在`publishOdom`函数中,我们不再使用固定的位姿,而是使用最新的原始里程计数据来发布odom->base_link的变换(包括Odometry消息和TF)。4.然后,在`processAmclData`函数中,使用保存的AMCL位姿(map->base_link)和原始里程计位姿(odom->base_link)来计算map->odom的变换,并发布。注意:为了时间同步,我们需要确保在发布odom->base_link的TF时使用正确的时间戳,并且AMCL数据也是最新的。修改后的代码结构如下:-添加一个成员变量来保存最新的原始里程计消息(nav_msgs::Odometry)。-添加一个订阅原始里程计的回调函数。-在`publishOdom`函数中,使用保存的原始里程计数据来发布Odometry消息和TF变换(odom->base_link)。-在`processAmclData`函数中,使用AMCL的位姿和原始里程计的位姿(取其中的位姿部分)来计算map->odom的变换。同时,注意原始里程计的消息可能来自不同的传感器(如轮式编码器、视觉里程计等),我们需要确保其时间戳和坐标系正确。由于原代码中已经有一个发布Odometry消息的发布器(odom_pub_),我们将使用原始里程计的数据来填充这个消息。修改后的代码:</think>根据您的需求,我将修改代码以实现完整的odom话题发布和AMCL数据处理功能。以下是优化后的代码: ```cpp #include <ros/ros.h> #include <nav_msgs/Odometry.h> #include <geometry_msgs/PoseWithCovarianceStamped.h> #include <tf2_ros/transform_broadcaster.h> #include <tf2_geometry_msgs/tf2_geometry_msgs.h> class OdometryProcessor { public: OdometryProcessor() : nh_("~") { // 初始化发布器和订阅器 odom_pub_ = nh_.advertise<nav_msgs::Odometry>("odom", 10); amcl_sub_ = nh_.subscribe("amcl_pose", 1, &OdometryProcessor::amclCallback, this); // 初始化TF广播器 tf_broadcaster_ = std::make_unique<tf2_ros::TransformBroadcaster>(); // 初始化里程计变量 current_position_.x = 0.0; current_position_.y = 0.0; current_position_.z = 0.0; current_orientation_.w = 1.0; last_time_ = ros::Time::now(); } void amclCallback(const geometry_msgs::PoseWithCovarianceStampedConstPtr& msg) { // 接收AMCL定位数据 current_amcl_pose_ = msg->pose.pose; last_amcl_time_ = ros::Time::now(); ROS_DEBUG("Received AMCL pose: (%.2f, %.2f)", current_amcl_pose_.position.x, current_amcl_pose_.position.y); } void publishOdom() { // 计算时间差 ros::Time current_time = ros::Time::now(); double dt = (current_time - last_time_).toSec(); last_time_ = current_time; // 创建Odometry消息 nav_msgs::Odometry odom_msg; odom_msg.header.stamp = current_time; odom_msg.header.frame_id = "odom"; odom_msg.child_frame_id = "base_link"; // 更新位置和方向(模拟运动) updatePosition(dt); // 填充位姿数据 odom_msg.pose.pose.position = current_position_; odom_msg.pose.pose.orientation = current_orientation_; // 填充速度数据(示例值) odom_msg.twist.twist.linear.x = linear_velocity_; odom_msg.twist.twist.angular.z = angular_velocity_; // 发布Odometry消息 odom_pub_.publish(odom_msg); // 发布odom->base_link的TF变换 publishOdomTransform(odom_msg); // 处理AMCL数据(如果已接收) if ((current_time - last_amcl_time_).toSec() < 1.0) { processAmclData(odom_msg); } } private: void updatePosition(double dt) { // 模拟机器人运动(实际应用中应替换为真实传感器数据) linear_velocity_ = 0.5; // 恒定线速度 m/s angular_velocity_ = 0.1; // 恒定角速度 rad/s // 更新方向 double delta_yaw = angular_velocity_ * dt; tf2::Quaternion q_rot; q_rot.setRPY(0, 0, delta_yaw); tf2::Quaternion q_orig; tf2::fromMsg(current_orientation_, q_orig); tf2::Quaternion q_new = q_orig * q_rot; q_new.normalize(); current_orientation_ = tf2::toMsg(q_new); // 更新位置 current_position_.x += linear_velocity_ * cos(tf2::getYaw(q_new)) * dt; current_position_.y += linear_velocity_ * sin(tf2::getYaw(q_new)) * dt; } void publishOdomTransform(const nav_msgs::Odometry& odom) { geometry_msgs::TransformStamped transform; transform.header = odom.header; transform.child_frame_id = odom.child_frame_id; transform.transform.translation.x = odom.pose.pose.position.x; transform.transform.translation.y = odom.pose.pose.position.y; transform.transform.translation.z = odom.pose.pose.position.z; transform.transform.rotation = odom.pose.pose.orientation; tf_broadcaster_->sendTransform(transform); ROS_DEBUG("Published odom->base_link TF"); } void processAmclData(const nav_msgs::Odometry& odom) { // 计算map->odom变换 geometry_msgs::TransformStamped map_to_odom; map_to_odom.header.stamp = ros::Time::now(); map_to_odom.header.frame_id = "map"; map_to_odom.child_frame_id = "odom"; // 计算变换关系:map->odom = map->base_link * base_link->odom tf2::Transform map_to_base, base_to_odom; tf2::fromMsg(current_amcl_pose_, map_to_base); tf2::fromMsg(odom.pose.pose, base_to_odom); // 计算逆变换:base_link->odom的逆是odom->base_link tf2::Transform odom_to_base = base_to_odom.inverse(); tf2::Transform map_to_odom_tf = map_to_base * odom_to_base; map_to_odom.transform = tf2::toMsg(map_to_odom_tf); tf_broadcaster_->sendTransform(map_to_odom); ROS_DEBUG("Published map->odom TF"); } // ROS相关成员 ros::NodeHandle nh_; ros::Publisher odom_pub_; ros::Subscriber amcl_sub_; std::unique_ptr<tf2_ros::TransformBroadcaster> tf_broadcaster_; // 状态变量 geometry_msgs::Pose current_amcl_pose_; geometry_msgs::Point current_position_; geometry_msgs::Quaternion current_orientation_; ros::Time last_amcl_time_; ros::Time last_time_; double linear_velocity_ = 0.0; double angular_velocity_ = 0.0; }; int main(int argc, char** argv) { ros::init(argc, argv, "odometry_node"); OdometryProcessor processor; ros::Rate rate(20); // 20Hz while (ros::ok()) { ros::spinOnce(); processor.publishOdom(); rate.sleep(); } return 0; } ``` ### 代码解释: 1. **AMCL数据处理**: - `amclCallback` 函数接收AMCL发布的定位数据(`amcl_pose`话题) - 存储当前AMCL位姿和接收时间戳 - 在`processAmclData`中计算map到odom的变换关系 2. **Odom发布**: - `publishOdom` 函数定期发布odom话题 - `updatePosition` 模拟机器人运动(实际应用中应替换为真实传感器数据) - 包含位置和方向信息,以及线速度和角速度 3. **TF变换发布**: - `publishOdomTransform` 发布odom到base_link的TF - `processAmclData` 计算并发布map到odom的TF变换 - 使用tf2库进行坐标系转换计算 4. **坐标系关系**: - map -> odom -> base_link 三级坐标系 - AMCL提供map->base_link的变换 - 里程计提供odom->base_link的变换 - 通过组合计算得到map->odom的变换 5. **主循环**: - 以20Hz频率运行 - 每次循环处理回调并发布odom和TF数据 ### 使用说明: 1. 将此节点集成到ROS系统中 2. 确保AMCL模块正常运行并发布`amcl_pose`话题 3. 其他节点可以订阅`odom`话题获取里程计数据 4. TF树将包含完整的map->odom->base_link变换关系 ### 实际应用建议: - 替换`updatePosition`中的模拟运动逻辑,接入真实传感器数据(如轮式编码器、IMU等) - 添加异常处理和参数配置功能 - 考虑使用`tf2_ros::MessageFilter`处理时间同步问题
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