之前博文《ROS学习笔记之——gmapping与amcl 》已经介绍过gmapping与amcl了。本博文详细的看一下amcl的代码。如需要修改amcl，用新的包，可以选择到/opt/ros/melodic/include/amcl目录下，把旧的amcl删掉，然后再编译新的，当然也可以通过更改名字（更改包的名字）。个人认为，更改名字是比较好的方法了哈～

AMCL源码

TF的数据类型

发布TF

订阅TF

关于TF的一些测试

参考资料

从之前博文以及实验中发现，amcl定位有两个关键点。

1，需要一个初始的姿态估计。初始的位姿估计并不需要很准确，大致的位置和orientation对了就可以了。

2，在移动的过程中，如果把amcl的点可视化出来，会发现随着机器人的移动，会逐渐收敛。（在之前博客中已经做过类似的仿真了，后来实验也可以得到类似的效果，但收敛的速度和最终收敛的大小，相对没有仿真的这么好）

amcl使用粒子滤波器在已知地图的情况下定位机器人位姿。它根据粒子集合的统计数据插入一定数量的新粒子，来解决全局定位失效或者说是机器人绑架的问题。并根据KLD采样自适应的调节粒子数量，可以在定位精度和计算代价之间进行权衡。

AMCL源码

打开AMCL的源码，会发现有大量的文档，但是关键的节点应该就是amcl_node.cpp文件

关于源码的解读见下面注释

https://gaoyichao.com/Xiaotu/?book=turtlebot&title=index#part6

https://gaoyichao.com/Xiaotu/?book=turtlebot&title=amcl%E7%9A%84%E6%80%BB%E4%BD%93%E4%B8%9A%E5%8A%A1%E9%80%BB%E8%BE%91

https://gaoyichao.com/Xiaotu/?book=turtlebot&title=amcl_demo%E4%B9%8B%E5%AE%9A%E4%BD%8D%E5%99%A8

通过turtlbot navigation中的amcl.launch文件定义了运行amcl的配置参数，

下面来看看amcl_node.cpp文件

/**  Copyright (c) 2008, Willow Garage, Inc.*  All rights reserved.**  This library is free software; you can redistribute it and/or*  modify it under the terms of the GNU Lesser General Public*  License as published by the Free Software Foundation; either*  version 2.1 of the License, or (at your option) any later version.**  This library is distributed in the hope that it will be useful,*  but WITHOUT ANY WARRANTY; without even the implied warranty of*  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU*  Lesser General Public License for more details.**  You should have received a copy of the GNU Lesser General Public*  License along with this library; if not, write to the Free Software*  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA**//* Author: Brian Gerkey */#include <algorithm>
#include <vector>
#include <map>
#include <cmath>
#include <memory>#include <boost/bind.hpp>
#include <boost/thread/mutex.hpp>// Signal handling
#include <signal.h>#include "amcl/map/map.h"
#include "amcl/pf/pf.h"
#include "amcl/sensors/amcl_odom.h"
#include "amcl/sensors/amcl_laser.h"
#include "portable_utils.hpp"#include "ros/assert.h"// roscpp
#include "ros/ros.h"// Messages that I need
#include "sensor_msgs/LaserScan.h"
#include "geometry_msgs/PoseWithCovarianceStamped.h"
#include "geometry_msgs/PoseArray.h"
#include "geometry_msgs/Pose.h"
#include "geometry_msgs/PoseStamped.h"
#include "nav_msgs/GetMap.h"
#include "nav_msgs/SetMap.h"
#include "std_srvs/Empty.h"// For transform support
#include "tf2/LinearMath/Transform.h"
#include "tf2/convert.h"
#include "tf2/utils.h"
#include "tf2_geometry_msgs/tf2_geometry_msgs.h"
#include "tf2_ros/buffer.h"
#include "tf2_ros/message_filter.h"
#include "tf2_ros/transform_broadcaster.h"
#include "tf2_ros/transform_listener.h"
#include "message_filters/subscriber.h"// Dynamic_reconfigure
#include "dynamic_reconfigure/server.h"
#include "amcl/AMCLConfig.h"// Allows AMCL to run from bag file
#include <rosbag/bag.h>
#include <rosbag/view.h>
#include <boost/foreach.hpp>// For monitoring the estimator
#include <diagnostic_updater/diagnostic_updater.h>#define NEW_UNIFORM_SAMPLING 1using namespace amcl;// Pose hypothesis 姿态的假设
typedef struct
{// Total weight (weights sum to 1)double weight;//姿态的权重。每个姿态有对应的权重来衡量其确信度// Mean of pose esimatepf_vector_t pf_pose_mean;//粒子估计姿态的均值// Covariance of pose estimatepf_matrix_t pf_pose_cov;//估计的协方差} amcl_hyp_t;static double
normalize(double z)
{return atan2(sin(z),cos(z));
}
static double
angle_diff(double a, double b)
{double d1, d2;a = normalize(a);b = normalize(b);d1 = a-b;d2 = 2*M_PI - fabs(d1);if(d1 > 0)d2 *= -1.0;if(fabs(d1) < fabs(d2))return(d1);elsereturn(d2);
}static const std::string scan_topic_ = "scan";//激光雷达的数据/* This function is only useful to have the whole code work* with old rosbags that have trailing slashes for their frames*/
inline
std::string stripSlash(const std::string& in)
{std::string out = in;if ( ( !in.empty() ) && (in[0] == '/') )out.erase(0,1);return out;
}class AmclNode
{public:AmclNode();~AmclNode();/*** @brief Uses TF and LaserScan messages from bag file to drive AMCL instead* @param in_bag_fn input bagfile* @param trigger_global_localization whether to trigger （触发） global localization* before starting to process the bagfile*/void runFromBag(const std::string &in_bag_fn, bool trigger_global_localization = false);int process();void savePoseToServer();private:std::shared_ptr<tf2_ros::TransformBroadcaster> tfb_;std::shared_ptr<tf2_ros::TransformListener> tfl_;std::shared_ptr<tf2_ros::Buffer> tf_;bool sent_first_transform_;tf2::Transform latest_tf_;bool latest_tf_valid_;// Pose-generating function used to uniformly distribute particles over// the mapstatic pf_vector_t uniformPoseGenerator(void* arg);
#if NEW_UNIFORM_SAMPLINGstatic std::vector<std::pair<int,int> > free_space_indices;
#endif// Callbacksbool globalLocalizationCallback(std_srvs::Empty::Request& req,std_srvs::Empty::Response& res);bool nomotionUpdateCallback(std_srvs::Empty::Request& req,std_srvs::Empty::Response& res);bool setMapCallback(nav_msgs::SetMap::Request& req,nav_msgs::SetMap::Response& res);void laserReceived(const sensor_msgs::LaserScanConstPtr& laser_scan);void initialPoseReceived(const geometry_msgs::PoseWithCovarianceStampedConstPtr& msg);void handleInitialPoseMessage(const geometry_msgs::PoseWithCovarianceStamped& msg);void mapReceived(const nav_msgs::OccupancyGridConstPtr& msg);void handleMapMessage(const nav_msgs::OccupancyGrid& msg);void freeMapDependentMemory();map_t* convertMap( const nav_msgs::OccupancyGrid& map_msg );void updatePoseFromServer();void applyInitialPose();//parameter for what odom to usestd::string odom_frame_id_;//paramater to store latest odom posegeometry_msgs::PoseStamped latest_odom_pose_;//parameter for what base to usestd::string base_frame_id_;std::string global_frame_id_;bool use_map_topic_;bool first_map_only_;ros::Duration gui_publish_period;ros::Time save_pose_last_time;ros::Duration save_pose_period;geometry_msgs::PoseWithCovarianceStamped last_published_pose;map_t* map_;char* mapdata;int sx, sy;double resolution;message_filters::Subscriber<sensor_msgs::LaserScan>* laser_scan_sub_;tf2_ros::MessageFilter<sensor_msgs::LaserScan>* laser_scan_filter_;ros::Subscriber initial_pose_sub_;std::vector< AMCLLaser* > lasers_;std::vector< bool > lasers_update_;std::map< std::string, int > frame_to_laser_;// Particle filterpf_t *pf_;double pf_err_, pf_z_;bool pf_init_;pf_vector_t pf_odom_pose_;double d_thresh_, a_thresh_;int resample_interval_;int resample_count_;double laser_min_range_;double laser_max_range_;//Nomotion update controlbool m_force_update;  // used to temporarily let amcl update samples even when no motion occurs...AMCLOdom* odom_;AMCLLaser* laser_;ros::Duration cloud_pub_interval;ros::Time last_cloud_pub_time;// For slowing play-back when reading directly from a bag fileros::WallDuration bag_scan_period_;void requestMap();// Helper to get odometric pose from transform systembool getOdomPose(geometry_msgs::PoseStamped& pose,double& x, double& y, double& yaw,const ros::Time& t, const std::string& f);//time for tolerance on the published transform,//basically defines how long a map->odom transform is good forros::Duration transform_tolerance_;ros::NodeHandle nh_;ros::NodeHandle private_nh_;ros::Publisher pose_pub_;ros::Publisher particlecloud_pub_;ros::ServiceServer global_loc_srv_;ros::ServiceServer nomotion_update_srv_; //to let amcl update samples without requiring motionros::ServiceServer set_map_srv_;ros::Subscriber initial_pose_sub_old_;ros::Subscriber map_sub_;diagnostic_updater::Updater diagnosic_updater_;void standardDeviationDiagnostics(diagnostic_updater::DiagnosticStatusWrapper& diagnostic_status);double std_warn_level_x_;double std_warn_level_y_;double std_warn_level_yaw_;amcl_hyp_t* initial_pose_hyp_;bool first_map_received_;bool first_reconfigure_call_;boost::recursive_mutex configuration_mutex_;dynamic_reconfigure::Server<amcl::AMCLConfig> *dsrv_;amcl::AMCLConfig default_config_;ros::Timer check_laser_timer_;int max_beams_, min_particles_, max_particles_;double alpha1_, alpha2_, alpha3_, alpha4_, alpha5_;double alpha_slow_, alpha_fast_;double z_hit_, z_short_, z_max_, z_rand_, sigma_hit_, lambda_short_;//beam skip related paramsbool do_beamskip_;double beam_skip_distance_, beam_skip_threshold_, beam_skip_error_threshold_;double laser_likelihood_max_dist_;odom_model_t odom_model_type_;double init_pose_[3];double init_cov_[3];laser_model_t laser_model_type_;bool tf_broadcast_;bool selective_resampling_;void reconfigureCB(amcl::AMCLConfig &config, uint32_t level);ros::Time last_laser_received_ts_;ros::Duration laser_check_interval_;void checkLaserReceived(const ros::TimerEvent& event);
};#if NEW_UNIFORM_SAMPLING
std::vector<std::pair<int,int> > AmclNode::free_space_indices;
#endif#define USAGE "USAGE: amcl"boost::shared_ptr<AmclNode> amcl_node_ptr;//定义一个amcl节点的指针void sigintHandler(int sig)
{// Save latest pose as we're shutting down.amcl_node_ptr->savePoseToServer();ros::shutdown();
}//主函数
int
main(int argc, char** argv)
{ros::init(argc, argv, "amcl");ros::NodeHandle nh;// Override default sigint handlersignal(SIGINT, sigintHandler);// Make our node available to sigintHandleramcl_node_ptr.reset(new AmclNode());//通过指针来创建amcl类（AmclNode）if (argc == 1){// run using ROS inputros::spin();}else if ((argc >= 3) && (std::string(argv[1]) == "--run-from-bag")){if (argc == 3){amcl_node_ptr->runFromBag(argv[2]);}else if ((argc == 4) && (std::string(argv[3]) == "--global-localization")){amcl_node_ptr->runFromBag(argv[2], true);}}// Without this, our boost locks are not shut down nicelyamcl_node_ptr.reset();// To quote Morgan, Hooray!return(0);
}AmclNode::AmclNode() :sent_first_transform_(false),latest_tf_valid_(false),map_(NULL),pf_(NULL),resample_count_(0),odom_(NULL),laser_(NULL),private_nh_("~"),initial_pose_hyp_(NULL),first_map_received_(false),first_reconfigure_call_(true)
{boost::recursive_mutex::scoped_lock l(configuration_mutex_);// 读入一系列的参数// Grab params off the param serverprivate_nh_.param("use_map_topic", use_map_topic_, false);private_nh_.param("first_map_only", first_map_only_, false);double tmp;private_nh_.param("gui_publish_rate", tmp, -1.0);gui_publish_period = ros::Duration(1.0/tmp);private_nh_.param("save_pose_rate", tmp, 0.5);save_pose_period = ros::Duration(1.0/tmp);private_nh_.param("laser_min_range", laser_min_range_, -1.0);private_nh_.param("laser_max_range", laser_max_range_, -1.0);private_nh_.param("laser_max_beams", max_beams_, 30);private_nh_.param("min_particles", min_particles_, 100);private_nh_.param("max_particles", max_particles_, 5000);private_nh_.param("kld_err", pf_err_, 0.01);private_nh_.param("kld_z", pf_z_, 0.99);private_nh_.param("odom_alpha1", alpha1_, 0.2);private_nh_.param("odom_alpha2", alpha2_, 0.2);private_nh_.param("odom_alpha3", alpha3_, 0.2);private_nh_.param("odom_alpha4", alpha4_, 0.2);private_nh_.param("odom_alpha5", alpha5_, 0.2);private_nh_.param("do_beamskip", do_beamskip_, false);private_nh_.param("beam_skip_distance", beam_skip_distance_, 0.5);private_nh_.param("beam_skip_threshold", beam_skip_threshold_, 0.3);if (private_nh_.hasParam("beam_skip_error_threshold_")){private_nh_.param("beam_skip_error_threshold_", beam_skip_error_threshold_);}else{private_nh_.param("beam_skip_error_threshold", beam_skip_error_threshold_, 0.9);}private_nh_.param("laser_z_hit", z_hit_, 0.95);private_nh_.param("laser_z_short", z_short_, 0.1);private_nh_.param("laser_z_max", z_max_, 0.05);private_nh_.param("laser_z_rand", z_rand_, 0.05);private_nh_.param("laser_sigma_hit", sigma_hit_, 0.2);private_nh_.param("laser_lambda_short", lambda_short_, 0.1);private_nh_.param("laser_likelihood_max_dist", laser_likelihood_max_dist_, 2.0);std::string tmp_model_type;private_nh_.param("laser_model_type", tmp_model_type, std::string("likelihood_field"));if(tmp_model_type == "beam")laser_model_type_ = LASER_MODEL_BEAM;else if(tmp_model_type == "likelihood_field")laser_model_type_ = LASER_MODEL_LIKELIHOOD_FIELD;else if(tmp_model_type == "likelihood_field_prob"){laser_model_type_ = LASER_MODEL_LIKELIHOOD_FIELD_PROB;}else{ROS_WARN("Unknown laser model type \"%s\"; defaulting to likelihood_field model",tmp_model_type.c_str());laser_model_type_ = LASER_MODEL_LIKELIHOOD_FIELD;}private_nh_.param("odom_model_type", tmp_model_type, std::string("diff"));if(tmp_model_type == "diff")odom_model_type_ = ODOM_MODEL_DIFF;else if(tmp_model_type == "omni")odom_model_type_ = ODOM_MODEL_OMNI;else if(tmp_model_type == "diff-corrected")odom_model_type_ = ODOM_MODEL_DIFF_CORRECTED;else if(tmp_model_type == "omni-corrected")odom_model_type_ = ODOM_MODEL_OMNI_CORRECTED;else{ROS_WARN("Unknown odom model type \"%s\"; defaulting to diff model",tmp_model_type.c_str());odom_model_type_ = ODOM_MODEL_DIFF;}private_nh_.param("update_min_d", d_thresh_, 0.2);private_nh_.param("update_min_a", a_thresh_, M_PI/6.0);private_nh_.param("odom_frame_id", odom_frame_id_, std::string("odom"));private_nh_.param("base_frame_id", base_frame_id_, std::string("base_link"));private_nh_.param("global_frame_id", global_frame_id_, std::string("map"));private_nh_.param("resample_interval", resample_interval_, 2);private_nh_.param("selective_resampling", selective_resampling_, false);double tmp_tol;private_nh_.param("transform_tolerance", tmp_tol, 0.1);private_nh_.param("recovery_alpha_slow", alpha_slow_, 0.001);private_nh_.param("recovery_alpha_fast", alpha_fast_, 0.1);private_nh_.param("tf_broadcast", tf_broadcast_, true);// For diagnostics（诊断）private_nh_.param("std_warn_level_x", std_warn_level_x_, 0.2);private_nh_.param("std_warn_level_y", std_warn_level_y_, 0.2);private_nh_.param("std_warn_level_yaw", std_warn_level_yaw_, 0.1);transform_tolerance_.fromSec(tmp_tol);{double bag_scan_period;private_nh_.param("bag_scan_period", bag_scan_period, -1.0);bag_scan_period_.fromSec(bag_scan_period);}odom_frame_id_ = stripSlash(odom_frame_id_);base_frame_id_ = stripSlash(base_frame_id_);global_frame_id_ = stripSlash(global_frame_id_);updatePoseFromServer();cloud_pub_interval.fromSec(1.0);tfb_.reset(new tf2_ros::TransformBroadcaster());tf_.reset(new tf2_ros::Buffer());tfl_.reset(new tf2_ros::TransformListener(*tf_));pose_pub_ = nh_.advertise<geometry_msgs::PoseWithCovarianceStamped>("amcl_pose", 2, true);//发布的位姿信息particlecloud_pub_ = nh_.advertise<geometry_msgs::PoseArray>("particlecloud", 2, true);//发布的粒子滤波的信息//注册三个服务global_loc_srv_ = nh_.advertiseService("global_localization", &AmclNode::globalLocalizationCallback,this);//用于获取机器人的全局定位nomotion_update_srv_= nh_.advertiseService("request_nomotion_update", &AmclNode::nomotionUpdateCallback, this);//用于手动的触发粒子更新并发布新的位姿估计set_map_srv_= nh_.advertiseService("set_map", &AmclNode::setMapCallback, this);//设定机器人位姿和地图信息，调用setMapCallback//构建激光传感器的消息过滤器对象和tf2的过滤器，并注册回调函数laserReceived
// 这里的message_filter为ROS系统提供了一些通用的消息过滤方法
//它对接收到的消息进行缓存，只有当满足过滤条件后才输出，在需要消息同步的时候应用比较多。
//这里主要是同时监听激光扫描消息和里程计坐标变换，同步两者的输出。 laser_scan_sub_ = new message_filters::Subscriber<sensor_msgs::LaserScan>(nh_, scan_topic_, 100);//订阅激光雷达的信息laser_scan_filter_ = new tf2_ros::MessageFilter<sensor_msgs::LaserScan>(*laser_scan_sub_,*tf_,odom_frame_id_,100,nh_);//调用laserReceived函数 ，在里面实现了将激光雷达信息进行tf变换                                                          laser_scan_filter_->registerCallback(boost::bind(&AmclNode::laserReceived,this, _1));订阅初始位姿，并调用initialPoseReceived函数                                                 initial_pose_sub_ = nh_.subscribe("initialpose", 2, &AmclNode::initialPoseReceived, this);//进行位姿的初始化//通过topic"initialpose"来对机器人的位姿做初始化估计if(use_map_topic_) {map_sub_ = nh_.subscribe("map", 1, &AmclNode::mapReceived, this);ROS_INFO("Subscribed to map topic.");} else {requestMap();}m_force_update = false;dsrv_ = new dynamic_reconfigure::Server<amcl::AMCLConfig>(ros::NodeHandle("~"));dynamic_reconfigure::Server<amcl::AMCLConfig>::CallbackType cb = boost::bind(&AmclNode::reconfigureCB, this, _1, _2);dsrv_->setCallback(cb);// 15s timer to warn on lack of receipt of laser scans, #5209laser_check_interval_ = ros::Duration(15.0);check_laser_timer_ = nh_.createTimer(laser_check_interval_, boost::bind(&AmclNode::checkLaserReceived, this, _1));diagnosic_updater_.setHardwareID("None");diagnosic_updater_.add("Standard deviation", this, &AmclNode::standardDeviationDiagnostics);
}void AmclNode::reconfigureCB(AMCLConfig &config, uint32_t level)
{boost::recursive_mutex::scoped_lock cfl(configuration_mutex_);//we don't want to do anything on the first call//which corresponds to startupif(first_reconfigure_call_){first_reconfigure_call_ = false;default_config_ = config;return;}if(config.restore_defaults) {config = default_config_;//avoid loopingconfig.restore_defaults = false;}d_thresh_ = config.update_min_d;a_thresh_ = config.update_min_a;resample_interval_ = config.resample_interval;laser_min_range_ = config.laser_min_range;laser_max_range_ = config.laser_max_range;gui_publish_period = ros::Duration(1.0/config.gui_publish_rate);save_pose_period = ros::Duration(1.0/config.save_pose_rate);transform_tolerance_.fromSec(config.transform_tolerance);max_beams_ = config.laser_max_beams;alpha1_ = config.odom_alpha1;alpha2_ = config.odom_alpha2;alpha3_ = config.odom_alpha3;alpha4_ = config.odom_alpha4;alpha5_ = config.odom_alpha5;z_hit_ = config.laser_z_hit;z_short_ = config.laser_z_short;z_max_ = config.laser_z_max;z_rand_ = config.laser_z_rand;sigma_hit_ = config.laser_sigma_hit;lambda_short_ = config.laser_lambda_short;laser_likelihood_max_dist_ = config.laser_likelihood_max_dist;if(config.laser_model_type == "beam")laser_model_type_ = LASER_MODEL_BEAM;else if(config.laser_model_type == "likelihood_field")laser_model_type_ = LASER_MODEL_LIKELIHOOD_FIELD;else if(config.laser_model_type == "likelihood_field_prob")laser_model_type_ = LASER_MODEL_LIKELIHOOD_FIELD_PROB;if(config.odom_model_type == "diff")odom_model_type_ = ODOM_MODEL_DIFF;else if(config.odom_model_type == "omni")odom_model_type_ = ODOM_MODEL_OMNI;else if(config.odom_model_type == "diff-corrected")odom_model_type_ = ODOM_MODEL_DIFF_CORRECTED;else if(config.odom_model_type == "omni-corrected")odom_model_type_ = ODOM_MODEL_OMNI_CORRECTED;if(config.min_particles > config.max_particles){ROS_WARN("You've set min_particles to be greater than max particles, this isn't allowed so they'll be set to be equal.");config.max_particles = config.min_particles;}min_particles_ = config.min_particles;max_particles_ = config.max_particles;alpha_slow_ = config.recovery_alpha_slow;alpha_fast_ = config.recovery_alpha_fast;tf_broadcast_ = config.tf_broadcast;do_beamskip_= config.do_beamskip; beam_skip_distance_ = config.beam_skip_distance; beam_skip_threshold_ = config.beam_skip_threshold; // Clear queued laser objects so that their parameters get updatedlasers_.clear();lasers_update_.clear();frame_to_laser_.clear();if( pf_ != NULL ){pf_free( pf_ );pf_ = NULL;}  pf_ = pf_alloc(min_particles_, max_particles_,alpha_slow_, alpha_fast_,(pf_init_model_fn_t)AmclNode::uniformPoseGenerator,(void *)map_);pf_set_selective_resampling(pf_, selective_resampling_);pf_err_ = config.kld_err; pf_z_ = config.kld_z; pf_->pop_err = pf_err_;pf_->pop_z = pf_z_;// Initialize the filterpf_vector_t pf_init_pose_mean = pf_vector_zero();pf_init_pose_mean.v[0] = last_published_pose.pose.pose.position.x;pf_init_pose_mean.v[1] = last_published_pose.pose.pose.position.y;pf_init_pose_mean.v[2] = tf2::getYaw(last_published_pose.pose.pose.orientation);pf_matrix_t pf_init_pose_cov = pf_matrix_zero();pf_init_pose_cov.m[0][0] = last_published_pose.pose.covariance[6*0+0];pf_init_pose_cov.m[1][1] = last_published_pose.pose.covariance[6*1+1];pf_init_pose_cov.m[2][2] = last_published_pose.pose.covariance[6*5+5];pf_init(pf_, pf_init_pose_mean, pf_init_pose_cov);pf_init_ = false;// Instantiate the sensor objects// Odometrydelete odom_;odom_ = new AMCLOdom();ROS_ASSERT(odom_);odom_->SetModel( odom_model_type_, alpha1_, alpha2_, alpha3_, alpha4_, alpha5_ );// Laserdelete laser_;laser_ = new AMCLLaser(max_beams_, map_);ROS_ASSERT(laser_);if(laser_model_type_ == LASER_MODEL_BEAM)laser_->SetModelBeam(z_hit_, z_short_, z_max_, z_rand_,sigma_hit_, lambda_short_, 0.0);else if(laser_model_type_ == LASER_MODEL_LIKELIHOOD_FIELD_PROB){ROS_INFO("Initializing likelihood field model; this can take some time on large maps...");laser_->SetModelLikelihoodFieldProb(z_hit_, z_rand_, sigma_hit_,laser_likelihood_max_dist_, do_beamskip_, beam_skip_distance_, beam_skip_threshold_, beam_skip_error_threshold_);ROS_INFO("Done initializing likelihood field model with probabilities.");}else if(laser_model_type_ == LASER_MODEL_LIKELIHOOD_FIELD){ROS_INFO("Initializing likelihood field model; this can take some time on large maps...");laser_->SetModelLikelihoodField(z_hit_, z_rand_, sigma_hit_,laser_likelihood_max_dist_);ROS_INFO("Done initializing likelihood field model.");}odom_frame_id_ = stripSlash(config.odom_frame_id);base_frame_id_ = stripSlash(config.base_frame_id);global_frame_id_ = stripSlash(config.global_frame_id);delete laser_scan_filter_;laser_scan_filter_ = new tf2_ros::MessageFilter<sensor_msgs::LaserScan>(*laser_scan_sub_,*tf_,odom_frame_id_,100,nh_);laser_scan_filter_->registerCallback(boost::bind(&AmclNode::laserReceived,this, _1));initial_pose_sub_ = nh_.subscribe("initialpose", 2, &AmclNode::initialPoseReceived, this);//通过函数initialPoseReceived订阅初始化的位姿
}void AmclNode::runFromBag(const std::string &in_bag_fn, bool trigger_global_localization)
{//根据信息记录包来运行amclrosbag::Bag bag;bag.open(in_bag_fn, rosbag::bagmode::Read);std::vector<std::string> topics;topics.push_back(std::string("tf"));std::string scan_topic_name = "base_scan"; // TODO determine what topic this actually is from ROStopics.push_back(scan_topic_name);rosbag::View view(bag, rosbag::TopicQuery(topics));ros::Publisher laser_pub = nh_.advertise<sensor_msgs::LaserScan>(scan_topic_name, 100);ros::Publisher tf_pub = nh_.advertise<tf2_msgs::TFMessage>("/tf", 100);//发布tf// Sleep for a second to let all subscribers connectros::WallDuration(1.0).sleep();ros::WallTime start(ros::WallTime::now());// Wait for mapwhile (ros::ok()){{boost::recursive_mutex::scoped_lock cfl(configuration_mutex_);if (map_){ROS_INFO("Map is ready");break;}}ROS_INFO("Waiting for map...");ros::getGlobalCallbackQueue()->callAvailable(ros::WallDuration(1.0));}if (trigger_global_localization){std_srvs::Empty empty_srv;globalLocalizationCallback(empty_srv.request, empty_srv.response);}BOOST_FOREACH(rosbag::MessageInstance const msg, view){if (!ros::ok()){break;}// Process any ros messages or callbacks at this pointros::getGlobalCallbackQueue()->callAvailable(ros::WallDuration());tf2_msgs::TFMessage::ConstPtr tf_msg = msg.instantiate<tf2_msgs::TFMessage>();if (tf_msg != NULL){tf_pub.publish(msg);for (size_t ii=0; ii<tf_msg->transforms.size(); ++ii){tf_->setTransform(tf_msg->transforms[ii], "rosbag_authority");}continue;}sensor_msgs::LaserScan::ConstPtr base_scan = msg.instantiate<sensor_msgs::LaserScan>();if (base_scan != NULL){laser_pub.publish(msg);laser_scan_filter_->add(base_scan);if (bag_scan_period_ > ros::WallDuration(0)){bag_scan_period_.sleep();}continue;}ROS_WARN_STREAM("Unsupported message type" << msg.getTopic());}bag.close();double runtime = (ros::WallTime::now() - start).toSec();ROS_INFO("Bag complete, took %.1f seconds to process, shutting down", runtime);const geometry_msgs::Quaternion & q(last_published_pose.pose.pose.orientation);double yaw, pitch, roll;tf2::Matrix3x3(tf2::Quaternion(q.x, q.y, q.z, q.w)).getEulerYPR(yaw,pitch,roll);ROS_INFO("Final location %.3f, %.3f, %.3f with stamp=%f",last_published_pose.pose.pose.position.x,last_published_pose.pose.pose.position.y,yaw, last_published_pose.header.stamp.toSec());ros::shutdown();
}void AmclNode::savePoseToServer()
{// We need to apply the last transform to the latest odom pose to get// the latest map pose to store.  We'll take the covariance from// last_published_pose.tf2::Transform odom_pose_tf2;tf2::convert(latest_odom_pose_.pose, odom_pose_tf2);tf2::Transform map_pose = latest_tf_.inverse() * odom_pose_tf2;double yaw = tf2::getYaw(map_pose.getRotation());ROS_DEBUG("Saving pose to server. x: %.3f, y: %.3f", map_pose.getOrigin().x(), map_pose.getOrigin().y() );//amcl初始化的位置设置为了地图的位置。private_nh_.setParam("initial_pose_x", map_pose.getOrigin().x());private_nh_.setParam("initial_pose_y", map_pose.getOrigin().y());private_nh_.setParam("initial_pose_a", yaw);private_nh_.setParam("initial_cov_xx", last_published_pose.pose.covariance[6*0+0]);private_nh_.setParam("initial_cov_yy", last_published_pose.pose.covariance[6*1+1]);private_nh_.setParam("initial_cov_aa", last_published_pose.pose.covariance[6*5+5]);
}void AmclNode::updatePoseFromServer()
{init_pose_[0] = 0.0;init_pose_[1] = 0.0;init_pose_[2] = 0.0;init_cov_[0] = 0.5 * 0.5;init_cov_[1] = 0.5 * 0.5;init_cov_[2] = (M_PI/12.0) * (M_PI/12.0);// Check for NAN on input from param server, #5239double tmp_pos;private_nh_.param("initial_pose_x", tmp_pos, init_pose_[0]);if(!std::isnan(tmp_pos))init_pose_[0] = tmp_pos;else ROS_WARN("ignoring NAN in initial pose X position");private_nh_.param("initial_pose_y", tmp_pos, init_pose_[1]);if(!std::isnan(tmp_pos))init_pose_[1] = tmp_pos;elseROS_WARN("ignoring NAN in initial pose Y position");private_nh_.param("initial_pose_a", tmp_pos, init_pose_[2]);if(!std::isnan(tmp_pos))init_pose_[2] = tmp_pos;elseROS_WARN("ignoring NAN in initial pose Yaw");private_nh_.param("initial_cov_xx", tmp_pos, init_cov_[0]);if(!std::isnan(tmp_pos))init_cov_[0] =tmp_pos;elseROS_WARN("ignoring NAN in initial covariance XX");private_nh_.param("initial_cov_yy", tmp_pos, init_cov_[1]);if(!std::isnan(tmp_pos))init_cov_[1] = tmp_pos;elseROS_WARN("ignoring NAN in initial covariance YY");private_nh_.param("initial_cov_aa", tmp_pos, init_cov_[2]);if(!std::isnan(tmp_pos))init_cov_[2] = tmp_pos;elseROS_WARN("ignoring NAN in initial covariance AA");
}void
AmclNode::checkLaserReceived(const ros::TimerEvent& event)
{ros::Duration d = ros::Time::now() - last_laser_received_ts_;if(d > laser_check_interval_){ROS_WARN("No laser scan received (and thus no pose updates have been published) for %f seconds.  Verify that data is being published on the %s topic.",d.toSec(),ros::names::resolve(scan_topic_).c_str());}
}void
AmclNode::requestMap()
{boost::recursive_mutex::scoped_lock ml(configuration_mutex_);// get map via RPCnav_msgs::GetMap::Request  req;nav_msgs::GetMap::Response resp;ROS_INFO("Requesting the map...");while(!ros::service::call("static_map", req, resp)){ROS_WARN("Request for map failed; trying again...");ros::Duration d(0.5);d.sleep();}handleMapMessage( resp.map );
}void
AmclNode::mapReceived(const nav_msgs::OccupancyGridConstPtr& msg)
{if( first_map_only_ && first_map_received_ ) {return;}handleMapMessage( *msg );first_map_received_ = true;
}void
AmclNode::handleMapMessage(const nav_msgs::OccupancyGrid& msg)
{boost::recursive_mutex::scoped_lock cfl(configuration_mutex_);ROS_INFO("Received a %d X %d map @ %.3f m/pix\n",msg.info.width,msg.info.height,msg.info.resolution);if(msg.header.frame_id != global_frame_id_)ROS_WARN("Frame_id of map received:'%s' doesn't match global_frame_id:'%s'. This could cause issues with reading published topics",msg.header.frame_id.c_str(),global_frame_id_.c_str());freeMapDependentMemory();// Clear queued laser objects because they hold pointers to the existing// map, #5202.lasers_.clear();lasers_update_.clear();frame_to_laser_.clear();map_ = convertMap(msg);#if NEW_UNIFORM_SAMPLING// Index of free spacefree_space_indices.resize(0);for(int i = 0; i < map_->size_x; i++)for(int j = 0; j < map_->size_y; j++)if(map_->cells[MAP_INDEX(map_,i,j)].occ_state == -1)free_space_indices.push_back(std::make_pair(i,j));
#endif// Create the particle filterpf_ = pf_alloc(min_particles_, max_particles_,alpha_slow_, alpha_fast_,(pf_init_model_fn_t)AmclNode::uniformPoseGenerator,(void *)map_);pf_set_selective_resampling(pf_, selective_resampling_);pf_->pop_err = pf_err_;pf_->pop_z = pf_z_;// Initialize the filterupdatePoseFromServer();pf_vector_t pf_init_pose_mean = pf_vector_zero();pf_init_pose_mean.v[0] = init_pose_[0];pf_init_pose_mean.v[1] = init_pose_[1];pf_init_pose_mean.v[2] = init_pose_[2];pf_matrix_t pf_init_pose_cov = pf_matrix_zero();pf_init_pose_cov.m[0][0] = init_cov_[0];pf_init_pose_cov.m[1][1] = init_cov_[1];pf_init_pose_cov.m[2][2] = init_cov_[2];pf_init(pf_, pf_init_pose_mean, pf_init_pose_cov);pf_init_ = false;// Instantiate the sensor objects// Odometrydelete odom_;odom_ = new AMCLOdom();ROS_ASSERT(odom_);odom_->SetModel( odom_model_type_, alpha1_, alpha2_, alpha3_, alpha4_, alpha5_ );// Laserdelete laser_;laser_ = new AMCLLaser(max_beams_, map_);ROS_ASSERT(laser_);if(laser_model_type_ == LASER_MODEL_BEAM)laser_->SetModelBeam(z_hit_, z_short_, z_max_, z_rand_,sigma_hit_, lambda_short_, 0.0);else if(laser_model_type_ == LASER_MODEL_LIKELIHOOD_FIELD_PROB){ROS_INFO("Initializing likelihood field model; this can take some time on large maps...");laser_->SetModelLikelihoodFieldProb(z_hit_, z_rand_, sigma_hit_,laser_likelihood_max_dist_, do_beamskip_, beam_skip_distance_, beam_skip_threshold_, beam_skip_error_threshold_);ROS_INFO("Done initializing likelihood field model.");}else{ROS_INFO("Initializing likelihood field model; this can take some time on large maps...");laser_->SetModelLikelihoodField(z_hit_, z_rand_, sigma_hit_,laser_likelihood_max_dist_);ROS_INFO("Done initializing likelihood field model.");}// In case the initial pose message arrived before the first map,// try to apply the initial pose now that the map has arrived.applyInitialPose();}void
AmclNode::freeMapDependentMemory()
{if( map_ != NULL ) {map_free( map_ );map_ = NULL;}if( pf_ != NULL ) {pf_free( pf_ );pf_ = NULL;}delete odom_;odom_ = NULL;delete laser_;laser_ = NULL;
}/*** Convert an OccupancyGrid map message into the internal* representation.  This allocates a map_t and returns it.*/
map_t*
AmclNode::convertMap( const nav_msgs::OccupancyGrid& map_msg )
{map_t* map = map_alloc();ROS_ASSERT(map);map->size_x = map_msg.info.width;map->size_y = map_msg.info.height;map->scale = map_msg.info.resolution;map->origin_x = map_msg.info.origin.position.x + (map->size_x / 2) * map->scale;map->origin_y = map_msg.info.origin.position.y + (map->size_y / 2) * map->scale;// Convert to player formatmap->cells = (map_cell_t*)malloc(sizeof(map_cell_t)*map->size_x*map->size_y);ROS_ASSERT(map->cells);for(int i=0;i<map->size_x * map->size_y;i++){if(map_msg.data[i] == 0)map->cells[i].occ_state = -1;else if(map_msg.data[i] == 100)map->cells[i].occ_state = +1;elsemap->cells[i].occ_state = 0;}return map;
}AmclNode::~AmclNode()
{delete dsrv_;freeMapDependentMemory();delete laser_scan_filter_;delete laser_scan_sub_;// TODO: delete everything allocated in constructor
}// 获取机器人当前的pose（x，y，方向）
//注意，此处是通过tf来获取的！！！！！！
bool
AmclNode::getOdomPose(geometry_msgs::PoseStamped& odom_pose,double& x, double& y, double& yaw,const ros::Time& t, const std::string& f)
{//这里所谓的odom_pose其实就是odom->base_link
//odom->base_link就是以轮式里程计来看base_link的运动
//里程计开始计数的位置// Get the robot's posegeometry_msgs::PoseStamped ident;ident.header.frame_id = stripSlash(f);ident.header.stamp = t;tf2::toMsg(tf2::Transform::getIdentity(), ident.pose);try{this->tf_->transform(ident, odom_pose, odom_frame_id_);}catch(tf2::TransformException e){ROS_WARN("Failed to compute odom pose, skipping scan (%s)", e.what());return false;}x = odom_pose.pose.position.x;y = odom_pose.pose.position.y;yaw = tf2::getYaw(odom_pose.pose.orientation);return true;
}pf_vector_t
AmclNode::uniformPoseGenerator(void* arg)
{map_t* map = (map_t*)arg;
#if NEW_UNIFORM_SAMPLINGunsigned int rand_index = drand48() * free_space_indices.size();std::pair<int,int> free_point = free_space_indices[rand_index];pf_vector_t p;p.v[0] = MAP_WXGX(map, free_point.first);p.v[1] = MAP_WYGY(map, free_point.second);p.v[2] = drand48() * 2 * M_PI - M_PI;
#elsedouble min_x, max_x, min_y, max_y;min_x = (map->size_x * map->scale)/2.0 - map->origin_x;max_x = (map->size_x * map->scale)/2.0 + map->origin_x;min_y = (map->size_y * map->scale)/2.0 - map->origin_y;max_y = (map->size_y * map->scale)/2.0 + map->origin_y;pf_vector_t p;ROS_DEBUG("Generating new uniform sample");for(;;){p.v[0] = min_x + drand48() * (max_x - min_x);p.v[1] = min_y + drand48() * (max_y - min_y);p.v[2] = drand48() * 2 * M_PI - M_PI;// Check that it's a free cellint i,j;i = MAP_GXWX(map, p.v[0]);j = MAP_GYWY(map, p.v[1]);if(MAP_VALID(map,i,j) && (map->cells[MAP_INDEX(map,i,j)].occ_state == -1))break;}
#endifreturn p;
}bool
AmclNode::globalLocalizationCallback(std_srvs::Empty::Request& req,std_srvs::Empty::Response& res)
{if( map_ == NULL ) {return true;}boost::recursive_mutex::scoped_lock gl(configuration_mutex_);ROS_INFO("Initializing with uniform distribution");pf_init_model(pf_, (pf_init_model_fn_t)AmclNode::uniformPoseGenerator,(void *)map_);ROS_INFO("Global initialisation done!");pf_init_ = false;return true;
}// force nomotion updates (amcl updating without requiring motion)
//无需运动即可更新amcl
bool
AmclNode::nomotionUpdateCallback(std_srvs::Empty::Request& req,std_srvs::Empty::Response& res)
{m_force_update = true;//ROS_INFO("Requesting no-motion update");return true;
}bool
AmclNode::setMapCallback(nav_msgs::SetMap::Request& req,nav_msgs::SetMap::Response& res)
{handleMapMessage(req.map);handleInitialPoseMessage(req.initial_pose);//对位置初始化res.success = true;return true;
}//实现将激光雷达信息的tf转换
//Amcl的业务逻辑总体就是在一个四五百行的巨大函数laserReceived中实现的
///这个函数才是最重要的！！！！！！
void
AmclNode::laserReceived(const sensor_msgs::LaserScanConstPtr& laser_scan)
{//函数以雷达扫描数据的指针为输入参数std::string laser_scan_frame_id = stripSlash(laser_scan->header.frame_id);//从扫描数据中获取雷达消息ID//同时用变量last_laser_received_ts_记录下当前的系统时间， 它用于判定是否长时间未接收到雷达数据last_laser_received_ts_ = ros::Time::now();if( map_ == NULL ) {//如果没有地图，也会直接退出return;}boost::recursive_mutex::scoped_lock lr(configuration_mutex_);int laser_index = -1;//AmclNode借助一些vector和map的容器，来支持多传感器// Do we have the base->base_laser Tx yet?通过在frame_to_laser_中查找雷达的坐标ID//如果之前没有收到该雷达的消息，将新建一个对象保存在lasers_中，// 并相应的在lasers_update_中添加对应更新状态， 建立映射关系。if(frame_to_laser_.find(laser_scan_frame_id) == frame_to_laser_.end()){ROS_DEBUG("Setting up laser %d (frame_id=%s)\n", (int)frame_to_laser_.size(), laser_scan_frame_id.c_str());lasers_.push_back(new AMCLLaser(*laser_));//记录下当前构建的雷达对象lasers_update_.push_back(true);//标记雷达的更新状态laser_index = frame_to_laser_.size();geometry_msgs::PoseStamped ident;ident.header.frame_id = laser_scan_frame_id;ident.header.stamp = ros::Time();tf2::toMsg(tf2::Transform::getIdentity(), ident.pose);geometry_msgs::PoseStamped laser_pose;try{this->tf_->transform(ident, laser_pose, base_frame_id_);}catch(tf2::TransformException& e){ROS_ERROR("Couldn't transform from %s to %s, ""even though the message notifier is in use",laser_scan_frame_id.c_str(),base_frame_id_.c_str());return;}pf_vector_t laser_pose_v;laser_pose_v.v[0] = laser_pose.pose.position.x;laser_pose_v.v[1] = laser_pose.pose.position.y;// laser mounting angle gets computed later -> set to 0 here!laser_pose_v.v[2] = 0;lasers_[laser_index]->SetLaserPose(laser_pose_v);ROS_DEBUG("Received laser's pose wrt robot: %.3f %.3f %.3f",laser_pose_v.v[0],laser_pose_v.v[1],laser_pose_v.v[2]);frame_to_laser_[laser_scan_frame_id] = laser_index;} else {//直接通过frame_to_laser_获取雷达对象的索引// we have the laser pose, retrieve laser indexlaser_index = frame_to_laser_[laser_scan_frame_id];//直接通过frame_to_laser_获取雷达对象的索引}// Where was the robot when this scan was taken?pf_vector_t pose;//AmclNode通过成员函数getOdomPose获取接收到雷达数据时刻的里程计位姿if(!getOdomPose(latest_odom_pose_, pose.v[0], pose.v[1], pose.v[2],laser_scan->header.stamp, base_frame_id_)){ROS_ERROR("Couldn't determine robot's pose associated with laser scan");return;}pf_vector_t delta = pf_vector_zero();
//如果里程计的数据显示机器人已经发生了明显的位移或者旋转，就标记所有的雷达更新标记为true。
//pf_init_这个应该只是初始化的标志位if(pf_init_){// Compute change in pose//看看机器人是否已经发生了明显的位移或旋转//delta = pf_vector_coord_sub(pose, pf_odom_pose_);delta.v[0] = pose.v[0] - pf_odom_pose_.v[0];delta.v[1] = pose.v[1] - pf_odom_pose_.v[1];delta.v[2] = angle_diff(pose.v[2], pf_odom_pose_.v[2]);// See if we should update the filterbool update = fabs(delta.v[0]) > d_thresh_ ||fabs(delta.v[1]) > d_thresh_ ||fabs(delta.v[2]) > a_thresh_;update = update || m_force_update;m_force_update=false;// Set the laser update flagsif(update)for(unsigned int i=0; i < lasers_update_.size(); i++)lasers_update_[i] = true;}bool force_publication = false;if(!pf_init_){// Pose at last filter updatepf_odom_pose_ = pose;// Filter is now initializedpf_init_ = true;// Should update sensor datafor(unsigned int i=0; i < lasers_update_.size(); i++)lasers_update_[i] = true;force_publication = true;resample_count_ = 0;}// If the robot has moved, update the filterelse if(pf_init_ && lasers_update_[laser_index]){//根据刚刚更新标识，我们调用里程计对象的UpdateAction接口完成运动更新。//这应该是粒子滤波中，做状态更新的//printf("pose\n");//pf_vector_fprintf(pose, stdout, "%.3f");AMCLOdomData odata;odata.pose = pose;// HACK// Modify the delta in the action data so the filter gets// updated correctlyodata.delta = delta;// Use the action data to update the filterodom_->UpdateAction(pf_, (AMCLSensorData*)&odata);// Pose at last filter update//this->pf_odom_pose = pose;}bool resampled = false;// If the robot has moved, update the filterif(lasers_update_[laser_index]){//接下来，我们根据激光的扫描数据更新滤波器AMCLLaserData ldata;//首先构建AMCLLaserData对象ldata.sensor = lasers_[laser_index];//指定传感器对象ldata.range_count = laser_scan->ranges.size();//指定传感器量程//下面实现将接收到的传感器数据拷贝到ldata对象中// To account for lasers that are mounted upside-down, we determine the// min, max, and increment angles of the laser in the base frame.//// Construct min and max angles of laser, in the base_link frame.tf2::Quaternion q;q.setRPY(0.0, 0.0, laser_scan->angle_min);geometry_msgs::QuaternionStamped min_q, inc_q;min_q.header.stamp = laser_scan->header.stamp;min_q.header.frame_id = stripSlash(laser_scan->header.frame_id);tf2::convert(q, min_q.quaternion);q.setRPY(0.0, 0.0, laser_scan->angle_min + laser_scan->angle_increment);inc_q.header = min_q.header;tf2::convert(q, inc_q.quaternion);try{tf_->transform(min_q, min_q, base_frame_id_);tf_->transform(inc_q, inc_q, base_frame_id_);}catch(tf2::TransformException& e){ROS_WARN("Unable to transform min/max laser angles into base frame: %s",e.what());return;}double angle_min = tf2::getYaw(min_q.quaternion);double angle_increment = tf2::getYaw(inc_q.quaternion) - angle_min;// wrapping angle to [-pi .. pi]angle_increment = fmod(angle_increment + 5*M_PI, 2*M_PI) - M_PI;ROS_DEBUG("Laser %d angles in base frame: min: %.3f inc: %.3f", laser_index, angle_min, angle_increment);// Apply range min/max thresholds, if the user supplied themif(laser_max_range_ > 0.0)ldata.range_max = std::min(laser_scan->range_max, (float)laser_max_range_);elseldata.range_max = laser_scan->range_max;double range_min;if(laser_min_range_ > 0.0)range_min = std::max(laser_scan->range_min, (float)laser_min_range_);elserange_min = laser_scan->range_min;// The AMCLLaserData destructor will free this memoryldata.ranges = new double[ldata.range_count][2];ROS_ASSERT(ldata.ranges);for(int i=0;i<ldata.range_count;i++){// amcl doesn't (yet) have a concept of min range.  So we'll map short// readings to max range.if(laser_scan->ranges[i] <= range_min)ldata.ranges[i][0] = ldata.range_max;elseldata.ranges[i][0] = laser_scan->ranges[i];// Compute bearingldata.ranges[i][1] = angle_min +(i * angle_increment);}//最后通过激光传感器对象的UpdateSensor接口完成粒子滤波器的测量更新。lasers_[laser_index]->UpdateSensor(pf_, (AMCLSensorData*)&ldata);//前面是状态预测，这里是测量更新lasers_update_[laser_index] = false;//然后清除lasers_update_的标记pf_odom_pose_ = pose;//并更新滤波器的里程计位姿。（此处的粒子滤波的姿态是优化后的）“pf_odom_pose_”// Resample the particles//根据重采样计数器和设定的阈值，触发对滤波器的重采样if(!(++resample_count_ % resample_interval_)){pf_update_resample(pf_);resampled = true;}至此，amcl的主业务逻辑就基本结束了。//剩下的内容就是：获取当前的粒子集合，从中提炼出机器人的位姿估计，并将这些消息通过各个主题发布出去。pf_sample_set_t* set = pf_->sets + pf_->current_set;ROS_DEBUG("Num samples: %d\n", set->sample_count);// Publish the resulting cloud// TODO: set maximum rate for publishingif (!m_force_update){geometry_msgs::PoseArray cloud_msg;cloud_msg.header.stamp = ros::Time::now();cloud_msg.header.frame_id = global_frame_id_;cloud_msg.poses.resize(set->sample_count);for(int i=0;i<set->sample_count;i++){cloud_msg.poses[i].position.x = set->samples[i].pose.v[0];cloud_msg.poses[i].position.y = set->samples[i].pose.v[1];cloud_msg.poses[i].position.z = 0;tf2::Quaternion q;q.setRPY(0, 0, set->samples[i].pose.v[2]);tf2::convert(q, cloud_msg.poses[i].orientation);}particlecloud_pub_.publish(cloud_msg);//将粒子点群数据发布出去}}if(resampled || force_publication){// Read out the current hypotheses（假设 ）double max_weight = 0.0;int max_weight_hyp = -1;std::vector<amcl_hyp_t> hyps;hyps.resize(pf_->sets[pf_->current_set].cluster_count);for(int hyp_count = 0;hyp_count < pf_->sets[pf_->current_set].cluster_count; hyp_count++){double weight;pf_vector_t pose_mean;pf_matrix_t pose_cov;if (!pf_get_cluster_stats(pf_, hyp_count, &weight, &pose_mean, &pose_cov)){ROS_ERROR("Couldn't get stats on cluster %d", hyp_count);break;}hyps[hyp_count].weight = weight;hyps[hyp_count].pf_pose_mean = pose_mean;hyps[hyp_count].pf_pose_cov = pose_cov;if(hyps[hyp_count].weight > max_weight){max_weight = hyps[hyp_count].weight;max_weight_hyp = hyp_count;}}if(max_weight > 0.0){ROS_DEBUG("Max weight pose: %.3f %.3f %.3f",hyps[max_weight_hyp].pf_pose_mean.v[0],hyps[max_weight_hyp].pf_pose_mean.v[1],hyps[max_weight_hyp].pf_pose_mean.v[2]);/*puts("");pf_matrix_fprintf(hyps[max_weight_hyp].pf_pose_cov, stdout, "%6.3f");puts("");*/geometry_msgs::PoseWithCovarianceStamped p;//这个是amcl要发布的“amcl_pose”// Fill in the headerp.header.frame_id = global_frame_id_;p.header.stamp = laser_scan->header.stamp;// Copy in the posep.pose.pose.position.x = hyps[max_weight_hyp].pf_pose_mean.v[0];p.pose.pose.position.y = hyps[max_weight_hyp].pf_pose_mean.v[1];tf2::Quaternion q;//四元素q.setRPY(0, 0, hyps[max_weight_hyp].pf_pose_mean.v[2]);tf2::convert(q, p.pose.pose.orientation);// Copy in the covariance, converting from 3-D to 6-Dpf_sample_set_t* set = pf_->sets + pf_->current_set;for(int i=0; i<2; i++){for(int j=0; j<2; j++){// Report the overall filter covariance, rather than the// covariance for the highest-weight cluster//p.covariance[6*i+j] = hyps[max_weight_hyp].pf_pose_cov.m[i][j];p.pose.covariance[6*i+j] = set->cov.m[i][j];}}// Report the overall filter covariance, rather than the// covariance for the highest-weight cluster//p.covariance[6*5+5] = hyps[max_weight_hyp].pf_pose_cov.m[2][2];p.pose.covariance[6*5+5] = set->cov.m[2][2];/*printf("cov:\n");for(int i=0; i<6; i++){for(int j=0; j<6; j++)printf("%6.3f ", p.covariance[6*i+j]);puts("");}*/pose_pub_.publish(p);//将位姿发布出来last_published_pose = p;//AMCL算法是根据给定的地图，结合粒子滤波获取最佳定位点Mp，//这个定位点是相对于地图map上的坐标,也就是base_link（也就是机器人的基坐标）相对map上的坐标。// odom 的原点是机器人启动时刻的位置，它在map上的位置或转换矩阵是未知的//AMCL可以根据最佳粒子的位置推算出 odom->map（就是说通过最佳粒子推算出机器人在地图的中的位置）的tf转换信息并发布到 tf主题上//因为base_link->odom的tf转换信息是每时每刻都在发布的，所以它是已知的//故此，此处有如下的关系：//map->base_link（就是地图中机器人的位置  是根据最佳粒子推算的）//base_link->odom（这是现实中机器人的位姿可以说是里程计的信息），由turtlebot3_core发布//故此有：//机器人的里程计的信息 = 当前地图中的机器人的的位置    减去  地图中机器人的起点位置// 转为公式可以写成 ：map->odom = map->base_link   -  base_link->odom//或者写为：base_link->odom = map->base_link - map->odom    //所谓的base_link->odom是里程计开始计数的位置，用里程计来看base_link的运动//就是amcl中宣称，它检测的map->base_link是准的//然后odom->base_link，这个里面其实是已经有了odom的累计误差了//那前者减去后者。那就是把累积误差也减去了，所以得到的map->odom就准确了//就是累计误差已经在odom->base_link中体现了，所以map->odom就没有累积误差ROS_DEBUG("New pose: %6.3f %6.3f %6.3f",hyps[max_weight_hyp].pf_pose_mean.v[0],hyps[max_weight_hyp].pf_pose_mean.v[1],hyps[max_weight_hyp].pf_pose_mean.v[2]);//hyps[max_weight_hyp].pf_pose_mean.v[0], [1], [2] 就代表了粒子滤波获取最佳定位点Mp//也就是机器人的位姿那么位姿的格式是（x,y,theta）最后一个是yaw偏航角，// subtracting base to odom from map to base and send map to odom insteadgeometry_msgs::PoseStamped odom_to_map;//要发布的，从odom到map上的try{tf2::Quaternion q;//定义一个新的四元素q.setRPY(0, 0, hyps[max_weight_hyp].pf_pose_mean.v[2]);tf2::Transform tmp_tf(q, tf2::Vector3(hyps[max_weight_hyp].pf_pose_mean.v[0],hyps[max_weight_hyp].pf_pose_mean.v[1],0.0));
tmp_tf = 从map原点看base_link的位置  为yaw生成四元数，最后的0.0是（x,y,z）的Z的值为0  因为这是在二维平面中geometry_msgs::PoseStamped tmp_tf_stamped;tmp_tf_stamped.header.frame_id = base_frame_id_;tmp_tf_stamped.header.stamp = laser_scan->header.stamp;tf2::toMsg(tmp_tf.inverse(), tmp_tf_stamped.pose);
tmp_tf.inverse()=最佳定位点Mp为坐标的原点，地图map原点相对于Mp的位置，就是在base_link坐标系下map原点的位置this->tf_->transform(tmp_tf_stamped, odom_to_map, odom_frame_id_);//进行 base_link坐标系下的点转换到odom坐标系，//也就是把map原点转换到odom坐标系下，等于从odom原点看map原点的位置。放到latest_tf_变量里面}catch(tf2::TransformException){ROS_DEBUG("Failed to subtract base to odom transform");return;}tf2::convert(odom_to_map.pose, latest_tf_);latest_tf_valid_ = true;if (tf_broadcast_ == true){// We want to send a transform that is good up until a// tolerance time so that odom can be usedros::Time transform_expiration = (laser_scan->header.stamp +transform_tolerance_);geometry_msgs::TransformStamped tmp_tf_stamped;tmp_tf_stamped.header.frame_id = global_frame_id_;tmp_tf_stamped.header.stamp = transform_expiration;tmp_tf_stamped.child_frame_id = odom_frame_id_;tf2::convert(latest_tf_.inverse(), tmp_tf_stamped.transform);this->tfb_->sendTransform(tmp_tf_stamped);sent_first_transform_ = true;}}else{ROS_ERROR("No pose!");}}else if(latest_tf_valid_){if (tf_broadcast_ == true){// Nothing changed, so we'll just republish the last transform, to keep// everybody happy.ros::Time transform_expiration = (laser_scan->header.stamp +transform_tolerance_);geometry_msgs::TransformStamped tmp_tf_stamped;tmp_tf_stamped.header.frame_id = global_frame_id_;tmp_tf_stamped.header.stamp = transform_expiration;tmp_tf_stamped.child_frame_id = odom_frame_id_;tf2::convert(latest_tf_.inverse(), tmp_tf_stamped.transform);this->tfb_->sendTransform(tmp_tf_stamped);}// Is it time to save our last pose to the param serverros::Time now = ros::Time::now();if((save_pose_period.toSec() > 0.0) &&(now - save_pose_last_time) >= save_pose_period){this->savePoseToServer();save_pose_last_time = now;}}diagnosic_updater_.update();
}//订阅初始位姿，并调用initialPoseReceived函数
//接收初始化的位姿数据
void
AmclNode::initialPoseReceived(const geometry_msgs::PoseWithCovarianceStampedConstPtr& msg)
{handleInitialPoseMessage(*msg);//调用处理函数
}//进行的位姿初始化
//代码部分实现将坐标变换到map对应的坐标中的功能
//输入参数为一个pose
//http://docs.ros.org/en/melodic/api/geometry_msgs/html/msg/PoseWithCovarianceStamped.html
void
AmclNode::handleInitialPoseMessage(const geometry_msgs::PoseWithCovarianceStamped& msg)
{//根据接收的初始位姿消息，在该位姿附近高斯采样重新生成粒子
//只接受global_frame_id_（一般为map）的坐标，并重新生成粒子。在接收到的初始位姿附近采样生成粒子集。boost::recursive_mutex::scoped_lock prl(configuration_mutex_);if(msg.header.frame_id == ""){// This should be removed at some pointROS_WARN("Received initial pose with empty frame_id.  You should always supply a frame_id.");}// We only accept initial pose estimates in the global frame, #5148.else if(stripSlash(msg.header.frame_id) != global_frame_id_){ROS_WARN("Ignoring initial pose in frame \"%s\"; initial poses must be in the global frame, \"%s\"",stripSlash(msg.header.frame_id).c_str(),global_frame_id_.c_str());return;}// In case the client sent us a pose estimate in the past, integrate the// intervening odometric change.geometry_msgs::TransformStamped tx_odom;try{ros::Time now = ros::Time::now();// wait a little for the latest tf to become availabletx_odom = tf_->lookupTransform(base_frame_id_, msg.header.stamp,base_frame_id_, ros::Time::now(),odom_frame_id_, ros::Duration(0.5));}catch(tf2::TransformException e){// If we've never sent a transform, then this is normal, because the// global_frame_id_ frame doesn't exist.  We only care about in-time// transformation for on-the-move pose-setting, so ignoring this// startup condition doesn't really cost us anything.if(sent_first_transform_)ROS_WARN("Failed to transform initial pose in time (%s)", e.what());tf2::convert(tf2::Transform::getIdentity(), tx_odom.transform);}tf2::Transform tx_odom_tf2;tf2::convert(tx_odom.transform, tx_odom_tf2);tf2::Transform pose_old, pose_new;tf2::convert(msg.pose.pose, pose_old);pose_new = pose_old * tx_odom_tf2;// Transform into the global frameROS_INFO("Setting pose (%.6f): %.3f %.3f %.3f",ros::Time::now().toSec(),pose_new.getOrigin().x(),pose_new.getOrigin().y(),tf2::getYaw(pose_new.getRotation()));// Re-initialize the filterpf_vector_t pf_init_pose_mean = pf_vector_zero();pf_init_pose_mean.v[0] = pose_new.getOrigin().x();pf_init_pose_mean.v[1] = pose_new.getOrigin().y();pf_init_pose_mean.v[2] = tf2::getYaw(pose_new.getRotation());pf_matrix_t pf_init_pose_cov = pf_matrix_zero();// Copy in the covariance, converting from 6-D to 3-Dfor(int i=0; i<2; i++){for(int j=0; j<2; j++){pf_init_pose_cov.m[i][j] = msg.pose.covariance[6*i+j];}}pf_init_pose_cov.m[2][2] = msg.pose.covariance[6*5+5];delete initial_pose_hyp_;initial_pose_hyp_ = new amcl_hyp_t();initial_pose_hyp_->pf_pose_mean = pf_init_pose_mean;initial_pose_hyp_->pf_pose_cov = pf_init_pose_cov;applyInitialPose();
}/*** If initial_pose_hyp_ and map_ are both non-null, apply the initial* pose to the particle filter state.  initial_pose_hyp_ is deleted* and set to NULL after it is used.*/
void
AmclNode::applyInitialPose()
{boost::recursive_mutex::scoped_lock cfl(configuration_mutex_);if( initial_pose_hyp_ != NULL && map_ != NULL ) {pf_init(pf_, initial_pose_hyp_->pf_pose_mean, initial_pose_hyp_->pf_pose_cov);pf_init_ = false;delete initial_pose_hyp_;initial_pose_hyp_ = NULL;}
}void
AmclNode::standardDeviationDiagnostics(diagnostic_updater::DiagnosticStatusWrapper& diagnostic_status)
{double std_x = sqrt(last_published_pose.pose.covariance[6*0+0]);double std_y = sqrt(last_published_pose.pose.covariance[6*1+1]);double std_yaw = sqrt(last_published_pose.pose.covariance[6*5+5]);diagnostic_status.add("std_x", std_x);diagnostic_status.add("std_y", std_y);diagnostic_status.add("std_yaw", std_yaw);diagnostic_status.add("std_warn_level_x", std_warn_level_x_);diagnostic_status.add("std_warn_level_y", std_warn_level_y_);diagnostic_status.add("std_warn_level_yaw", std_warn_level_yaw_);if (std_x > std_warn_level_x_ || std_y > std_warn_level_y_ || std_yaw > std_warn_level_yaw_){diagnostic_status.summary(diagnostic_msgs::DiagnosticStatus::WARN, "Too large");}else{diagnostic_status.summary(diagnostic_msgs::DiagnosticStatus::OK, "OK");}
}

TF

在amcl中，tf同样是非常重要的。

http://wiki.ros.org/tf/Overview

TF的数据类型

作为ROS中的特色，TF定义了它自己的数据类型

tf的消息类型为geometry_msgs/TransformStamped

std_mags/Header headeruint32 seqtime stampstring frame_id
string child_frame_id
geometry_msgs/Transform transformgeometry_msgs/Vector3 translationfloat64 xfloat64 yfloat64 zgeometry_msgs/Quaternion rotationfloat64 xfloat64 yflaot64 zfloat64 w

发布TF

为了在一个节点中发布tf，需要采用tf::TransformBroadcaster

首先需要构建一个tf的发布者

tf::TransformBroadcaster();

然后发送

void sendTransform(const StampedTransform & transform);
void sendTransform(const geometry_msgs::TransformStamped & transform);

例程：下面代码将发布一个坐标帧到tf2上，随着turtles的运动，发布其坐标帧

   1 #include <ros/ros.h>2 #include <tf2/LinearMath/Quaternion.h>3 #include <tf2_ros/transform_broadcaster.h>  //发布pose到tf2的头文件4 #include <geometry_msgs/TransformStamped.h>5 #include <turtlesim/Pose.h>6 7 std::string turtle_name;8 9 void poseCallback(const turtlesim::PoseConstPtr& msg){10   static tf2_ros::TransformBroadcaster br;//创建tf的发布者对象11   geometry_msgs::TransformStamped transformStamped;  //要发布的信息
//关于这个消息类型可以参考：http://docs.ros.org/en/jade/api/geometry_msgs/html/msg/TransformStamped.html12   //往transformStamped里面加数据13   transformStamped.header.stamp = ros::Time::now();//需要给要发布的tf一个timestamp（时间戳）14   transformStamped.header.frame_id = "world";//设置父帧的名字15   transformStamped.child_frame_id = turtle_name;//设置子帧的名字（此处设置为自己）16   transformStamped.transform.translation.x = msg->x;17   transformStamped.transform.translation.y = msg->y;18   transformStamped.transform.translation.z = 0.0;19   tf2::Quaternion q;//定义四元数20   q.setRPY(0, 0, msg->theta);21   transformStamped.transform.rotation.x = q.x();22   transformStamped.transform.rotation.y = q.y();23   transformStamped.transform.rotation.z = q.z();24   transformStamped.transform.rotation.w = q.w();25 26   br.sendTransform(transformStamped);//然后发送出去（包含了位置和方向）27 }28 29 int main(int argc, char** argv){30   ros::init(argc, argv, "my_tf2_broadcaster");31 32   ros::NodeHandle private_node("~");33   if (! private_node.hasParam("turtle"))34   {35     if (argc != 2){ROS_ERROR("need turtle name as argument"); return -1;};36     turtle_name = argv[1];37   }38   else39   {40     private_node.getParam("turtle", turtle_name);41   }42     43   ros::NodeHandle node;44   ros::Subscriber sub = node.subscribe(turtle_name+"/pose", 10, &poseCallback);45 46   ros::spin();47   return 0;48 };

参考：http://wiki.ros.org/tf2/Tutorials/Writing%20a%20tf2%20broadcaster%20%28C%2B%2B%29#CA-655844a832971675d8cca09ac2412a96f7a2c2c6_1

订阅TF

例程：

#include <ros/ros.h>
#include <tf2_ros/transform_listener.h>//transform_listener就是接收tf的接收器
#include <geometry_msgs/TransformStamped.h>
#include <geometry_msgs/Twist.h>
#include <turtlesim/Spawn.h>int main(int argc, char** argv){ros::init(argc, argv, "my_tf2_listener");//初始化ROS节点ros::NodeHandle node;//创建节点句柄ros::service::waitForService("spawn");ros::ServiceClient spawner =node.serviceClient<turtlesim::Spawn>("spawn");turtlesim::Spawn turtle;turtle.request.x = 4;turtle.request.y = 2;turtle.request.theta = 0;turtle.request.name = "turtle2";spawner.call(turtle);// 创建发布turtle2速度控制指令的发布者ros::Publisher turtle_vel =node.advertise<geometry_msgs::Twist>("turtle2/cmd_vel", 10);//发布者tf2_ros::Buffer tfBuffer;tf2_ros::TransformListener tfListener(tfBuffer);//创建TransformListener的对象
//一旦tf的接收者创建成功，就会开始订阅tfros::Rate rate(10.0);while (node.ok()){geometry_msgs::TransformStamped transformStamped;//接收的tf样本try{获取turtle1与turtle2坐标系之间的tf数据transformStamped = tfBuffer.lookupTransform("turtle2", "turtle1",ros::Time(0));}catch (tf2::TransformException &ex) {ROS_WARN("%s",ex.what());ros::Duration(1.0).sleep();continue;}geometry_msgs::Twist vel_msg;// 根据turtle1与turtle2坐标系之间的位置关系，发布turtle2的速度控制指令vel_msg.angular.z = 4.0 * atan2(transformStamped.transform.translation.y,transformStamped.transform.translation.x);vel_msg.linear.x = 0.5 * sqrt(pow(transformStamped.transform.translation.x, 2) +pow(transformStamped.transform.translation.y, 2));turtle_vel.publish(vel_msg);rate.sleep();}return 0;
};

参考：http://wiki.ros.org/tf2/Tutorials/Writing%20a%20tf2%20listener%20%28C%2B%2B%29

关于TF的一些测试

我们先来看看正常开启机器人的tf树和开启amcl后的tf树有什么区别

正常开启机器人的时候，由turtlebot3_core发布了一个odom到base——footprint的tf

若由amcl进行定位。可以看到由amcl发布了一个由map到odom的

若由robot_localization进行定位

若同时发布两个回怎么样呢？

结果发现显示的东东跟上面一样。

单纯开启gmapping的时候的tf

参考资料

http://wiki.ros.org/tf/Overview

https://www.guyuehome.com/279

https://gaoyichao.com/Xiaotu/?book=turtlebot&title=amcl%E7%9A%84%E6%80%BB%E4%BD%93%E4%B8%9A%E5%8A%A1%E9%80%BB%E8%BE%91

https://www.cnblogs.com/li-yao7758258/p/7672521.html

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ROS学习笔记之——amcl源码的解读

AMCL源码

TF

TF的数据类型

发布TF

订阅TF

关于TF的一些测试

参考资料

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