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Rplidar学习(五)——rplidar使用cartographer_ros进行地图云生成

一、Cartographer简介Cartographer是google开源的通用2D和3D定位与地图同步构建的SLAM工具,并提供ROS接口。官网地址:https:github.comgoo

一、Cartographer简介

  Cartographer是google开源的通用2D和3D定位与地图同步构建的SLAM工具,并提供ROS接口。官网地址:https://github.com/googlecartographer

 

二、安装方法

1、安装全部依赖项

sudo apt-get update
sudo apt-get install -y \
cmake \
g
++ \
git \
google
-mock \
libboost
-all-dev \
libeigen3
-dev \
libgflags
-dev \
libgoogle
-glog-dev \
liblua5.
2-dev \
libprotobuf
-dev \
libsuitesparse
-dev \
libwebp
-dev \
ninja
-build \
protobuf
-compiler \
python
-sphinx \
ros
-kinetic-tf2-eigen \
libatlas
-base-dev \
libsuitesparse
-dev \
liblapack
-dev

 

 

2、安装ceres solver,下载安装在主目录下,由于googlesource.com需要FQ,这里使用hitcm(张明明)的github地址

# Build and install Ceres.
# git clone https:
//ceres-solver.googlesource.com/ceres-solver
# cd ceres-solver
git clone https:
//github.com/hitcm/ceres-solver-1.11.0.git
cd ceres-solver-1.11.0
mkdir build
cd build
cmake ..
-G Ninja
ninja
ninja test
sudo ninja install

 

 

3、安装cartographer,下载安装在主目录下,这里同样使用的是hitcm(张明明)的github地址

# Build and install Cartographer.
git clone https:
//github.com/hitcm/cartographer.git
cd cartographer
mkdir build
cd build
cmake ..
-G Ninja
ninja
ninja test
sudo ninja install

 

 

4、安装cartographer_ros,这里使用的是hitcm(张明明)的github地址,由于google官方的教程需要FQ下载一些文件,因此容易失败,经验证hitcm(张明明)对原文件进行了少许修改后可以成功安装,在他的修改中核心代码不变,只修改了编译文件。

# Install wstool and rosdep.
sudo apt-get update
sudo apt-get install -y python-wstool python-rosdep ninja-build
# Create a new workspace
in 'catkin_ws'.
mkdir catkin_ws
cd catkin_ws
wstool init src
# 下载到catkin_ws下面的src文件夹下面
cd src
git clone https:
//github.com/hitcm/cartographer_ros.git
# 然后到catkin_ws下面运行catkin_make安装 (会失败,所以根据提示改变命令)
cd
cd catkin_ws
catkin_make
source .
/devel/setup.zsh

 

 

5、改变命令进行编译

catkin_make_isolated --install --use-ninja

 

 

6、修改cartographer_ros--cartographer_ros--launch--demo_revo_lds.launch

      

"/use_sim_time" value="true" />
"cartographer_node" pkg="cartographer_ros"
type
="cartographer_node" args="
-configuration_directory $(find cartographer_ros)/configuration_files
-configuration_basename revo_lds.lua"
output="screen">
"scan" to="scan" />

"rviz" pkg="rviz" type="rviz" required="true"
args
="-d $(find cartographer_ros)/configuration_files/demo_2d.rviz" />

 

 

  修改cartographer_ros--cartographer_ros--configuration_files--revo_lds.lua

 

options = {

map_builder
= MAP_BUILDER,

sensor_bridge
= {

horizontal_laser_min_range
= 0.3,

horizontal_laser_max_range
= 8,

horizontal_laser_missing_echo_ray_length
= 1.2,

constant_odometry_translational_variance
= 0.,

constant_odometry_rotational_variance
= 0.,

},

map_frame
= "map",

tracking_frame
= "laser",

published_frame
= "laser",

odom_frame
= "odom",

provide_odom_frame
= true,

use_odometry_data
= false,

use_constant_odometry_variance
= true,

constant_odometry_translational_variance
= 1e-2,

constant_odometry_rotational_variance
= 1e-1,

use_horizontal_laser
= true,

use_horizontal_multi_echo_laser
= false,

horizontal_laser_min_range
= 0.1,

horizontal_laser_max_range
= 30.,

horizontal_laser_missing_echo_ray_length
= 5.,

num_lasers_3d
= 0,

lookup_transform_timeout_sec
= 0.2,

submap_publish_period_sec
= 0.3,

pose_publish_period_sec
= 5e-3,

}

MAP_BUILDER.use_trajectory_builder_2d
= true

TRAJECTORY_BUILDER_2D.use_imu_data
= false

TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching
= true

SPARSE_POSE_GRAPH.optimization_problem.huber_scale
= 1e2

return options

 

 

 

 

   修改完以上2个文件重新编译一下,命令行输入

 

catkin_make_isolated --install --use-ninja

 

 

 

 7、运行

  最后命令行中运行rplidar的Node和launch文件

 

roslaunch rplidar_ros rplidar.launch 
roslaunch cartographer_ros demo_revo_lds.launch

 

 

 

 

 

 

 

 


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