cartographer 3D点云建图教程

This article was original written by XRBLS, welcome re-post， but please keep this copyright info, thanks, any question could be asked via wechat: jintianiloveu

无人驾驶或者机器人，很重要的一环是建图，通过3D点云来完成，或者扫地机器人里面的单线激光雷达来完成。本文详细记录一下采用个cartographer来完成的建图步骤。整个过程简单，异形。首先安装一些依赖。

cartographer 依赖

安装 ceres solver:

git clone https://github.com/BlueWhaleRobot/ceres-solver.git
cd ceres-solver
mkdir build
cd build
cmake ..
make -j
sudo make install

安装protobuf 3.0:

git clone https://github.com/google/protobuf.git
cd protobuf
git checkout v3.6.1
mkdir build
cd build
cmake  \-DCMAKE_POSITION_INDEPENDENT_CODE=ON \-DCMAKE_BUILD_TYPE=Release \-Dprotobuf_BUILD_TESTS=OFF \../cmake
make -j 2
sudo make install

安装cartographer: 请注意，关于cartographer的安装，不要用cmake，要用ninja，另外据说master branch已经崩溃（谷歌垃圾程序员不知道搞什么事情），checkout release-1.0版本。

git clone https://github.com/googlecartographer/cartographer.git
cd cartographer
mkdir build
cd build
cmake .. -G Ninja
ninja
CTEST_OUTPUT_ON_FAILURE=1 ninja test
sudo ninja install

这里其实可以用官方的cartographer，不过用蓝经机器人的也行。至于各种区别，日后在研究研究。

最后，需要安装一下ros的wrapper。

mkdir catkin_ws
cd catkin_ws
wstool init src
wstool merge -t src https://raw.githubusercontent.com/googlecartographer/cartographer_ros/master/cartographer_ros.rosinstall
wstool update -t srccatkin_make_isolated --install --use-ninja

开始测试

ok，所有依赖安装完之后，再来测试一下。关于测试可以通过下载一个2D的博物馆的rosbag包进行离线的测试，最终会先生成一个pbstream的文件，然后使用cartographer自带的节点，将pbstream生成map。

rosrun cartographer_ros cartographer_pbstream_to_ros_map  -map_filestem=./aa_map -pbstream_filename=/media/jintain/wd/ros/slam/rosbags/cartographer_paper_deutsches_museum.bag.pbstream

这种离线的方式便可以将离线计算的pbstream结果，转化成需要的地图文件。但是本次教程的重要部分是总结一下，如何使用自己的数据进行建图，在线建图，然后将结果保存起来。

使用cartographer在自己的雷达数据上建立3D地图

分步骤来，假设已经安装好了cartographer。

编写自己的launch文件，启动cartographer，加载配置文件，话题remap，udrf位姿等

首先需要配置一些文件，需要的文件包括：a) demo_my_rslidar_3d.launch：启动文件； b) my_rslidar_3d.launch: 配置文件，话题remap，等。

其中 my_rslidar_3d.launch 最为重要，它包含的内容可以大致写为：

<launch><param name="robot_description"textfile="$(find cartographer_ros)/urdf/rslidar_2d.urdf" /><node name="robot_state_publisher" pkg="robot_state_publisher"type="robot_state_publisher" /><!-- <node pkg="tf" type="static_transform_publisher" name="imulink_broadcaster" args="0.07 -0.03 0 0 0 0 1 laserbase_link imu 50"/> --><node name="cartographer_node" pkg="cartographer_ros"type="cartographer_node" args="-configuration_directory $(find cartographer_ros)/configuration_files-configuration_basename my_3d.lua"output="screen"><remap from="/odom" to="/xqserial_server/Odom" /><remap from="/imu" to="/xqserial_server/IMU" /><remap from="/points2" to="/rslidar_points" /></node><node name="cartographer_occupancy_grid_node" pkg="cartographer_ros"type="cartographer_occupancy_grid_node" args="-resolution 0.05" />
</launch>

也就是说，制定urdf（位姿转换），发布状态，话题的remap，以及添加配置文件，其中cartographer定位需要的传感器数据就是， odom里程计， imu数据，激光雷达数据三种。

启动cartographer开始建图

这一步比较简单，直接启动上面的demo launch就行了。
保存建图状态

在线见图的时候，不会自动保存状态到本地，需要调用服务，来将状态保存。在rosbag播放完成之后，调用i一个service保存数据到本地。

先调用 rosrun rqt_service_caller rqt_service_caller /finish_trajectory 结束建图。

然后再调用，/write_state, 开始写入状态。保存成功之后，test_3d.pbfile 可以在 ~/.ros 目录下找到（这个也太隐蔽了吧）。

请注意，上面的操作是一个窗口，可以可视化操作。

在得到pbstream文件之后，可以进一步导出为ply的3D点云

此时，cartographer可以关掉了，因为我们已经拿到了pbstream文件，但是还需要将这个pbstream文件进一步转化成3D的ply点云文件。
```
 roslaunch cartographer_ros assets_writer_my_rslidar_3d.launch bag_filenames:=`pwd`/../rosbags/2018-08-11-13-20-34.bag    pose_graph_filename:=`pwd`/../rosbags/test_3d.pbstream
```

可视化保存的点云

由于最后生成的是ply的格式，接下来得将其转换为pcd，然后用pcl来可视化一下，看看最终点云地图张啥样。先将ply转换成pcd：

pcl_ply2pcd 2018-08-11-13-20-34.bag_points.ply test_3d.pcd

然后用下面的代码，可以方便的可视化pcd：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
// #include <pcl_visualization/cloud_viewer.h>
#include <pcl/visualization/cloud_viewer.h>int
main (int argc, char** argv)
{pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);if (pcl::io::loadPCDFile<pcl::PointXYZ> ("../ct_lx_rmp.pcd", *cloud) == -1) //* load the file{PCL_ERROR ("Couldn't read file test_pcd.pcd \n");return (-1);}std::cout << "Loaded "<< cloud->width * cloud->height<< " data points from test_pcd.pcd with the following fields: "<< std::endl;for (size_t i = 0; i < cloud->points.size (); ++i)std::cout << "    " << cloud->points[i].x<< " "    << cloud->points[i].y<< " "    << cloud->points[i].z << std::endl;// visualize itpcl::visualization::CloudViewer viewer("ff");viewer.showCloud(cloud);while (!viewer.wasStopped())     {}return (0);
}

CMakeLists.txt:

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)project(pcd_read)find_package(PCL REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable (pcd_read pcd_read.cpp)
target_link_libraries (pcd_read ${PCL_LIBRARIES})

当然，如果没有ply2pcd工具，直接编译 ply2pcd.cpp：

#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;void
printHelp (int, char **argv)
{print_error ("Syntax is: %s [-format 0|1] input.ply output.pcd\n", argv[0]);
}bool
loadCloud (const std::string &filename, pcl::PCLPointCloud2 &cloud)
{TicToc tt;print_highlight ("Loading "); print_value ("%s ", filename.c_str ());pcl::PLYReader reader;tt.tic ();if (reader.read (filename, cloud) < 0)return (false);print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (cloud).c_str ());return (true);
}void
saveCloud (const std::string &filename, const pcl::PCLPointCloud2 &cloud, bool format)
{TicToc tt;tt.tic ();print_highlight ("Saving "); print_value ("%s ", filename.c_str ());pcl::PCDWriter writer;writer.write (filename, cloud, Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), format);print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");
}/* ---[ */
int
main (int argc, char** argv)
{print_info ("Convert a PLY file to PCD format. For more information, use: %s -h\n", argv[0]);if (argc < 3){printHelp (argc, argv);return (-1);}// Parse the command line arguments for .pcd and .ply filesstd::vector<int> pcd_file_indices = parse_file_extension_argument (argc, argv, ".pcd");std::vector<int> ply_file_indices = parse_file_extension_argument (argc, argv, ".ply");if (pcd_file_indices.size () != 1 || ply_file_indices.size () != 1){print_error ("Need one input PLY file and one output PCD file.\n");return (-1);}// Command line parsingbool format = 1;parse_argument (argc, argv, "-format", format);print_info ("PCD output format: "); print_value ("%s\n", (format ? "binary" : "ascii"));// Load the first filepcl::PCLPointCloud2 cloud;if (!loadCloud (argv[ply_file_indices[0]], cloud)) return (-1);// Convert to PLY and savesaveCloud (argv[pcd_file_indices[0]], cloud, format);return (0);
}

可以很直观的得到点云的结果。

关于自己点云一些关键点

仔细观察会发现，这里面有一些重要因素，比如你需要一个urdf，你需要知道 lidar， imu想对于base link的转换关系，同时在进行将状态转换到ply的时候，你需要告诉assets writer一个关于机器人的配置文件。这个配置文件怎么弄呢？

我们来看看关于这个assets writer需要的配置文件.lua:

-- Copyright 2016 The Cartographer Authors
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--      http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.VOXEL_SIZE = 5e-2include "transform.lua"options = {tracking_frame = "laserbase_link",pipeline = {{action = "min_max_range_filter",min_range = 1.,max_range = 20.,},{action = "dump_num_points",},{action = "fixed_ratio_sampler",sampling_ratio = 0.01,},{action = "write_ply",filename = "points.ply"},-- Gray X-Rays. These only use geometry to color pixels.{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_yz_all",transform = YZ_TRANSFORM,},{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_xy_all",transform = XY_TRANSFORM,},{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_xz_all",transform = XZ_TRANSFORM,},-- Now we recolor our points by frame and write another batch of X-Rays. It-- is visible in them what was seen by the horizontal and the vertical-- laser.{action = "color_points",frame_id = "rslidar",color = { 255., 0., 0. },},{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_yz_all_color",transform = YZ_TRANSFORM,},{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_xy_all_color",transform = XY_TRANSFORM,},{action = "write_xray_image",voxel_size = VOXEL_SIZE,filename = "xray_xz_all_color",transform = XZ_TRANSFORM,},}
}return options

其实这里面修改的东西就几个，frameid，然后没了。。如果你采用rslida，也就是速腾的激光雷达，那这个基本上可以不用。