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ORB SLAM2 ros implementation.
We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with. However, the ORB-SLAM2 is based on the static-world assumption and still has a few shortcomings in handling with dynamic scene problems, so we integrate our method with the ORB-SLAM2 system in order to enhance its robustness and stability in highly dynamic environments.
- Maintainer status: maintained
- Maintainer: Lennart Haller
- Author:
- License: GPLv3
- Source: git https://github.com/appliedAI-Initiative/orb_slam_2_ros.git (branch: master)
Jenkins jobs (1)
ORB SLAM2 ros implementation.
- Maintainer status: maintained
- Maintainer: Lennart Haller
- Author:
- License: GPLv3
- Source: git https://github.com/appliedAI-Initiative/orb_slam_2_ros.git (branch: master)
Contents
- ORB-SLAM2 ROS node
- ROS parameters, topics and services
ORB-SLAM2 Authors:RaulMur-Artal,JuanD.Tardos, J.M.M.Montiel and DorianGalvez-Lopez (DBoW2). The original implementation can be found here.
This is a ROS implementation of the ORB-SLAM2 real-time SLAM library for Monocular, Stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. This implementation removes the Pangolin dependency, and the original viewer. All data I/O is handled via ROS topics. For vizualization you can use RViz.
Features
- Full ROS compatibility
- Supports a lot of cameras out of the box, such as the Intel RealSense family. See the run section for a list
- Data I/O via ROS topics
- Parameters can be set with the rqt_reconfigure gui during runtime
- Very quick startup through considerably sped up vocab file loading
- Full Map save and load functionality
ROS parameters, topics and services
Parameters
There are three types of parameters right now: static- and dynamic ros parameters and camera settings from the config file. The static parameters are send to the ROS parameter server at startup and are not supposed to change. They are set in the launch files which are located at ros/launch. The parameters are:
- load_map: Bool. If set to true, the node will try to load the map provided with map_file at startup.
- map_file: String. The name of the file the map is saved at.
- settings_file: String. The location of config file mentioned above.
- voc_file:String. The location of config vocanulary file mentioned above.
- publish_pose: Bool. If a PoseStamped message should be published. Even if this is false the tf will still be published.
- publish_pointcloud: Bool. If the pointcloud containing all key points (the map) should be published.
- pointcloud_frame_id: String. The Frame id of the Pointcloud/map.
- camera_frame_id: String. The Frame id of the camera position.
Dynamic parameters can be changed at runtime. Either by updating them directly via the command line or by using rqt_reconfigure which is the recommended way. The parameters are:
- localize_only: Bool. Toggle from/to only localization. The SLAM will then no longer add no new points to the map.
- reset_map: Bool. Set to true to erase the map and start new. After reset the parameter will automatically update back to false.
- min_num_kf_in_map: Int. Number of key frames a map has to have to not get reset after tracking is lost.
Finally, the intrinsic camera calibration parameters along with some hyperparameters can be found in the specific yaml files in orb_slam2/config.
Published topics
The following topics are being published and subscribed to by the nodes:
- All nodes publish (given the settings) a PointCloud2 containing all key points of the map.
- Live image from the camera containing the currently found key points and a status text.
- A tf from the pointcloud frame id to the camera frame id (the position).
Subscribed topics
- The mono node subscribes to /camera/image_raw for the input image.
- The RGBD node subscribes to /camera/rgb/image_raw for the RGB image and
- /camera/depth_registered/image_raw for the depth information.
- The stereo node subscribes to image_left/image_color_rect and
- image_right/image_color_rect for corresponding images.
Services
All nodes offer the possibility to save the map via the service node_type/save_map. So the save_map services are:
- /orb_slam2_rgbd/save_map
- /orb_slam2_mono/save_map
- /orb_slam2_stereo/save_map
ORB-SLAM2 Code (+ Theory) Analysis (1): Enter MONO_EUROC Main Function
tags: ORB-SLAM analysis
table of Contents
To understand the code implementation of a system, always start with the framework of the system, the system framework of the orb-slam2 is shown below:
We can see that Tracking lineCheng Zhong didExtract ORB features, initial position estimation, etc.Then this thread should belong to 'front-end VO' in the SLAM;Local mapping thread There are local bars, we can know that this thread belongs to the backend, mainly do backend optimization and building work;Loop closing threadIt can be judged from the literal meaning to be looped, divided into loop detection and looping correction. Then need to know that the TRACKING thread receives a frame of one frame after the picture starts working, and the keyframe received by the local mapping thread is from the Tracking thread 'Processing', the loop closing thread is also receiving local mapping thread 'further processing refining' of KeyFrame Come work.
note:Local mapping threadStartingKeyframe Operate!
Parameters
There are three types of parameters right now: static- and dynamic ros parameters and camera settings from the config file. The static parameters are send to the ROS parameter server at startup and are not supposed to change. They are set in the launch files which are located at ros/launch. The parameters are:
- load_map: Bool. If set to true, the node will try to load the map provided with map_file at startup.
- map_file: String. The name of the file the map is saved at.
- settings_file: String. The location of config file mentioned above.
- voc_file:String. The location of config vocanulary file mentioned above.
- publish_pose: Bool. If a PoseStamped message should be published. Even if this is false the tf will still be published.
- publish_pointcloud: Bool. If the pointcloud containing all key points (the map) should be published.
- pointcloud_frame_id: String. The Frame id of the Pointcloud/map.
- camera_frame_id: String. The Frame id of the camera position.
Dynamic parameters can be changed at runtime. Either by updating them directly via the command line or by using rqt_reconfigure which is the recommended way. The parameters are:
- localize_only: Bool. Toggle from/to only localization. The SLAM will then no longer add no new points to the map.
- reset_map: Bool. Set to true to erase the map and start new. After reset the parameter will automatically update back to false.
- min_num_kf_in_map: Int. Number of key frames a map has to have to not get reset after tracking is lost.
Finally, the intrinsic camera calibration parameters along with some hyperparameters can be found in the specific yaml files in orb_slam2/config.
Published topics
The following topics are being published and subscribed to by the nodes:
- All nodes publish (given the settings) a PointCloud2 containing all key points of the map.
- Live image from the camera containing the currently found key points and a status text.
- A tf from the pointcloud frame id to the camera frame id (the position).
Subscribed topics
- The mono node subscribes to /camera/image_raw for the input image.
- The RGBD node subscribes to /camera/rgb/image_raw for the RGB image and
- /camera/depth_registered/image_raw for the depth information.
- The stereo node subscribes to image_left/image_color_rect and
- image_right/image_color_rect for corresponding images.
Services
All nodes offer the possibility to save the map via the service node_type/save_map. So the save_map services are:
- /orb_slam2_rgbd/save_map
- /orb_slam2_mono/save_map
- /orb_slam2_stereo/save_map
ORB-SLAM2 Code (+ Theory) Analysis (1): Enter MONO_EUROC Main Function
tags: ORB-SLAM analysis
table of Contents
To understand the code implementation of a system, always start with the framework of the system, the system framework of the orb-slam2 is shown below:
We can see that Tracking lineCheng Zhong didExtract ORB features, initial position estimation, etc.Then this thread should belong to 'front-end VO' in the SLAM;Local mapping thread There are local bars, we can know that this thread belongs to the backend, mainly do backend optimization and building work;Loop closing threadIt can be judged from the literal meaning to be looped, divided into loop detection and looping correction. Then need to know that the TRACKING thread receives a frame of one frame after the picture starts working, and the keyframe received by the local mapping thread is from the Tracking thread 'Processing', the loop closing thread is also receiving local mapping thread 'further processing refining' of KeyFrame Come work.
note:Local mapping threadStartingKeyframe Operate!
Orb Slam2 Pdf
Remaining problem:Place Recognition and MAP What is it? Next interpretation
Orb Slam 2 Pdf
mono_euroc.ccThe main functions of the file are as follows
cd orb_slam/src/ORB_SLAM2 // My program is placed in ORB_SLAM / SRC / ORB_SLAM2
Run the program input:
argv[0] argv[1] argv[2] argv[3] argv[4]
./Examples/Monocular/mono_eurocVocabulary/ORBvoc.txtExamples/Monocular/EuRoC.yaml /home/hltt3838/EuRoC_data/MH_01_easy/mav0/cam0/dataExamples/Monocular/EuRoC_TimeStamps/MH01.txt
analysis:The program is easy to understand, mainlyRead image information, initialization system, tracking images, etc.The thinking map of the program is shown below:
Learning program 1:void LoadImages( )
understanding:
The program is well written, but it is necessary to pay attention to how to read the image according to its own image data. The corresponding time series is based on its image data. The above program name is as follows. :
The following program is converted into a unit: seconds
vTimeStamps.push_back(t/1e9); // Conversion unit: second, specifically see MH01.TXT
Learning program 2: ORB_SLAM2::System SLAM( )
This program isSystem.cc Inside the program, the specific analysis will look at:https://mp.csdn.net/editor/html/115089937
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