openvslam ros版本编译

https://blog.csdn.net/plejahhshsh/article/details/126002268

 

1 确保ros cvbriage使用的版本和openvslam 以及dow2库使用的 opencv一样

（ros 直接装的指令 默认怎装了opencv4.2 导致和自己编译的openvslam3.4.9不匹配，虽然编译通过但是后面可视化段错误）

1-1 如何修改cv_briage的opencv依赖版本

https://i.cnblogs.com/posts/edit;postId=18394497

1-2 修改ros工程

 opencv修改为这个这里统一使用 opencv3.4.9

 

 

2 编译带指令

 

 

 编译

#ros版本编译指令
cd /home/dongdong/2project/1salm/GNSS_openvslam/ros
catkin_make \
-DBUILD_WITH_MARCH_NATIVE=ON \
-DUSE_PANGOLIN_VIEWER=ON \
-DUSE_SOCKET_PUBLISHER=OFF \
-DUSE_STACK_TRACE_LOGGER=ON \
-DBOW_FRAMEWORK=DBoW2 \
-DBUILD_TESTS=OFF 

 

　　

2 指令运行

首先

roscore

 

 

然后

 

 source devel/setup.bash

定位节点

rosrun openvslam run_localization \
-v /home/dongdong/2project/0data/NWPU/FHY_config/orb_vocab.dbow2 \
-i /home/dongdong/2project/0data/NWPU/images \
-c /home/dongdong/2project/0data/NWPU/FHY_config/GNSS_config.yaml  \
--map-db /home/dongdong/2project/0data/NWPU/Map_GNSS.msg

　　

 

编写脚本执行 参考

#!/bin/bash

#外部给与执行权限
#sudo chmod +x run_ros_nodes.sh
# conda activate gaussian_splatting

WORKSPACE_DIR="/home/dongdong/2project/1salm/GNSS_openvslam/ros" # 修改1-1 自己创建的ros节点工程catkin_make根目录
python_DIR="/home/dongdong/2project/1salm/GNSS_openvslam/ros/src/image_gaosi/src" # 修改1-2 自己创建的python脚本位置

config_DIR="/home/dongdong/2project/0data/NWPU/FHY_config/GNSS_config.yaml" # 修改1-3 数据集

conda_envs="/home/dongdong/1sorftware/1work/yes" # 修改2-1 自己的conda 安装路径
ROS_cv_briage_dir = "/home/dongdong/1sorftware/1work/opencv/catkin_ws_cv_bridge/devel/setup.bash" # 修改2-2 自己编译的cv_briage包节点，貌似不用也行 制定了依赖opencv3.4.9 而非自带4.2
echo $ROS_cv_briage_dir
conda_envs_int=$conda_envs"/etc/profile.d/conda.sh" # 不用改 conda自带初始化文件
echo $conda_envs_int
conda_envs_bin=$conda_envs"/envs/gaussian_splatting/bin" # 不用改 conda自带python安装位置 脚本中需要指定是conda特定的环境python而不是系统默认的
echo $conda_envs_bin
ROS_SETUP="/opt/ros/noetic/setup.bash" #不用改  安装时候添加到系统路径了 不需要每次都source 这里留着
#指定目录

# 启动 ROS Master 不用改
echo "Starting ROS 总结点..."
gnome-terminal -- bash -c "\
cd $WORKSPACE_DIR; source devel/setup.bash; \  
roscore; \
exec bash"

# 等待 ROS Master 启动
sleep 3

# 运行 C++ 发布节点
# echo "Running C++ 发布节点..."
# gnome-terminal -- bash -c "\
# cd $WORKSPACE_DIR; source devel/setup.bash; \
# rosrun openvslam run_slam  \
#-v /home/dongdong/2project/0data/NWPU/FHY_config/orb_vocab.dbow2 \
#-i /home/dongdong/2project/0data/NWPU/images \
#-c /home/dongdong/2project/0data/NWPU/FHY_config/GNSS_config.yaml  \
#--map-db /home/dongdong/2project/0data/NWPU/Map_GNSS.msg ; \
# exec bash"


# 运行 C++ 接收节点   
echo "Running C++ 接收节点..."
gnome-terminal -- bash -c "\
cd $WORKSPACE_DIR; source devel/setup.bash; \
source $ROS_cv_briage_dir; \
rosrun openvslam run_localization \
-v /home/dongdong/2project/0data/NWPU/FHY_config/orb_vocab.dbow2 \
-i /home/dongdong/2project/0data/NWPU/images \
-c /home/dongdong/2project/0data/NWPU/FHY_config/GNSS_config.yaml  \
--map-db /home/dongdong/2project/0data/NWPU/Map_GNSS.msg ; \

exec bash"






# 运行 python 渲染图节点
# source conda_envs_int 和 source ROS_cv_briage_dir 非必要，但是考虑到脚本经常因为系统环境默认变量找不到导致的路径问题，这里还是强制给了也便于学习了解执行流程。
echo "Running python 订阅节点..."
echo "1 激活conda本身(脚本执行需要) 2 激活conda环境  3运行python 节点 并跟上输入参数[训练模型保存根目录，指定要使用的模型训练次数,要测试的模型精度模式]"
gnome-terminal -- bash -c "\
source $conda_envs_int; \ 
source $ROS_cv_briage_dir; \ 
conda activate gaussian_splatting ; \
cd $python_DIR; \
python3 v1_image_pose_subscriber.py \
-m /home/dongdong/2project/0data/NWPU/gs_out/train1_out_sh0_num30000 \
--iteration 30000 \
--models baseline ;\
exec bash"

#$conda_envs_bin/python3 image_gps_subscriber.py \

　　

 

 

其他指令参考

1）将图像更改为话题发布出去
roscore
rosrun image_publisher image_publisher /opt/ros/melodic/share/rviz/images/splash.png
2）播放bag包，将指定的话题进行新话题名的映射
rosbag play 2023-03-15-19-28-26.bag /camera/infra1/image_rect_raw:=/camera/image_raw
3）显示图像的话题
rosrun image_view image_view image:=/image_publisher_1603025741590002479/image_raw
4）读取视频
rosrun image_publisher image_publisher  /xxx/1.mp4
5）读取摄像头数据，将参数改为摄像头设备号或者设备文件，执行以下指令：
rosrun image_publisher image_publisher  0
与以下指令等价：
rosrun image_publisher image_publisher  /dev/video0

　　

stella_vslam_ros是单独的代码，这里没用到

1）发布数据基于视频的方法
rosrun image_publisher image_publisher ./aist_living_lab_1/video.mp4 /image_raw:=/camera/image_raw
2）发布数据基于一般摄像头的
apt install ros-${ROS_DISTRO}-usb-cam
rosparam set usb_cam/pixel_format yuyv
rosrun usb_cam usb_cam_node
rosrun image_transport republish \
    raw in:=/usb_cam/image_raw raw out:=/camera/image_raw
3）运行（Tracking and Mapping   源码位置：stella_vslam_ros/src/run_slam.cc）
source ~/catkin_ws/devel/setup.bash
rosrun stella_vslam_ros run_slam \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-out /path/to/map.msg
4）运行（Localization 源码位置：stella_vslam_ros/src/run_slam.cc）
source ~/catkin_ws/devel/setup.bash
rosrun stella_vslam_ros run_slam \
    --disable-mapping \
    -v /path/to/orb_vocab.fbow \
    -c /path/to/config.yaml \
    --map-db-in /path/to/map.msg