Welcome to ROSMASTER X3 repository
11.9 ORB_SLAM2_Octomap
9. ORB_SLAM2_Octomap
9. ORB_SLAM2_Octomap9.1. Introduction9.2. Use9.3, octomap_server9.3.1, Topics and Services9.3.2. Configuration parameters9.3.3, TF transformation9.4. Expansion testing
octomap official website: http://octomap.github.io/
octomap source code: https://github.com/OctoMap/octomap
octomap wiki: http://wiki.ros.org/octomap
octomap_server: http://wiki.ros.org/octomap_server
9.1. Introduction
Octomap uses the octree data structure to store the probabilistic occupancy map of the three-dimensional environment. OctoMap library implements a 3D occupancy grid mapping method, providing data structures and mapping algorithms in C++, which is particularly suitable for robots. The map implementation is based on octree.
It elegantly compresses and updates maps with adjustable resolution! It stores maps in the form of octotree (will be discussed later), which can save a lot of space compared to point clouds. The map created by octomap probably looks like this: (different resolutions from left to right)
Precautions
Note: When building a map, moving the robot slowly and losing key frames may cause the map building to fail.
According to different models, you only need to set the purchased model in [.bashrc], X1 (normal four-wheel drive) X3 (Mailun) Take X3 as an example
xxxxxxxxxx#Raspberry Pi 5 master needs to enter docker first, please perform this step#If running the script into docker fails, please refer to ROS/07, Docker tutorial~/run_docker.shOpen the [.bashrc] file
xxxxxxxxxxsudo vim .bashrc
Find the [ROBOT_TYPE] parameters and modify the corresponding car model
xxxxxxxxxxexport ROBOT_TYPE=X3 # ROBOT_TYPE: X1 X3 X3plus R2 X79.2. Use
Start orb_slam and the underlying driver (Robot side)
xxxxxxxxxxroslaunch yahboomcar_slam robot_orb_slam.launch bUseViewer:=true
[bUseViewer] parameter: whether to open the visualization window of orbslam. If true, you can clearly view the key points. If the positioning is unsuccessful, you can reset the key points. Click [Reset] on the left side of the picture below.
<PI5 needs to open another terminal to enter the same docker container
Start octree mapping (Robot side)
xxxxxxxxxxroslaunch yahboomcar_slam robot_orb_octomap.launch frame_id:=odom use_rviz:=false
[frame_id] parameter: coordinate system name, available by default and no need to set.
[use_rviz] parameter: whether to enable rviz.
Turn on the visual interface (virtual machine side)
xxxxxxxxxxroslaunch yahboomcar_slam view_orb_octomap.launch
Due to the octree, its map looks like it is composed of many small squares (much like Minecraft). When the resolution is high, the squares are small; when the resolution is low, the squares are large. Each square represents the probability of that square being occupied.
9.3, octomap_server
9.3.1, Topics and Services
| Subscription topic | Type | Description |
|---|---|---|
| cloud_in | sensor_msgs/PointCloud2 | Incoming 3D point cloud for scan integration. |
| Post Topic | Type | Description |
| octomap_binary | octomap_msgs/Octomap | Complete maximum likelihood occupancy map as a compact octal map binary stream, encoding free space and occupied space. Binary messages only differentiate between free space and occupied space, but smaller. |
| octomap_full | octomap_msgs/Octomap | Full maximum likelihood occupancy map as a compact octal map binary stream, encoding free space and occupied space. The complete message contains the complete probabilities and all additional data stored in the tree. |
| occupied_cells_vis_array | visualization_msgs/MarkerArray | In RViz, all occupied voxels are marked as visualization "boxes" |
| octomap_point_cloud_centers | sensor_msgs/PointCloud2 | The centers of all occupied voxels serve as point clouds, useful for visualization. Note that there will be gaps as points have no volume size and octagonal voxels can have different resolutions! |
| map | nav_msgs/OccupancyGrid | Project a 2D occupancy map downward from a 3D map. |
| Service | Type | Description |
| octomap_binary | octomap_msgs/GetOctomap | The complete maximum likelihood occupancy map as a compact binary stream of octal maps, encoding free space and occupied space. |
| clear_bbx | octomap_msgs/BoundingBoxQuery | Clears an area in the 3D occupancy map, setting all voxels in the area to "free" |
| reset | std_srvs/Empty | Reset the entire map |
Node view
xxxxxxxxxxrqt_graph
9.3.2. Configuration parameters
| Parameters | Type | Default value | Description |
|---|---|---|---|
| frame_id | string | /map | The static global frame in which the map will be published. When building a map dynamically, sensor data needs to be converted to this frame. |
| resolution | float | 0.05 | The resolution of the map in meters when starting from an empty map. Otherwise the resolution of the loaded file will be used. |
| base_frame_id | string | base_footprint | The robot base that performs ground plane detection (if enabled) |
| height_map | bool | true | Whether the visualization should encode the height with different colors |
| color/[r/g/b/a] | float | When ~heigh_map=False, display the color of occupied cells in the [0:1] range | |
| sensor_model/max_range | float | -1 (unlimited) | The maximum range (in meters) for inserting point cloud data when building a map dynamically. Limiting the range to a useful range (e.g. 5 meters) prevents false error points away from the robot. |
| sensor_model/[hit|miss] | float | 0.7 / 0.4 | Hit and miss probabilities in the sensor model when building the map dynamically |
| sensor_model/[min|max] | float | 0.12 / 0.97 | Minimum and maximum probability of clamping when building a map dynamically |
| latch | bool | True for static mapping, false if no initial mapping is given | Whether the topic is locked for publishing or only published once per change. For best performance when building maps (which update frequently), set this to false. When set to true, all topics and visualizations will be created on all changes on each map. |
| filter_ground | bool | false | Whether the ground plane should be detected and ignored from scan data when dynamically building a map using pcl::SACMODEL_vertical_plane. This clears everything on the ground, but does not insert the ground into the map as an obstacle. If this feature is enabled, the ~ground_filter/... parameters can be further configured. |
| ground_filter/distance | float | 0.04 | The distance threshold of the point (z direction) to be segmented to the ground plane |
| ground_filter/angle | float | 0.15 | The angle threshold between the detected plane and the horizontal plane detected as the ground |
| ground_filter/plane_distance | float | 0.07 | Distance threshold at z=0 for a plane to be detected as ground (fourth coefficient of the PCL plane equation) |
| pointcloud[min|max]z | float | -/+ infinity | Insert the minimum and maximum height to be considered in the callback. |
| occupancy[min|max]z | float | -/+ infinity | The minimum and maximum height to be considered in the final map. |
9.3.3, TF transformation
| Required TF transformation | Description |
|---|---|
| data sensor frame --> /map | If scanning integration is done, the sensor data needs to be converted into a global map frame. This information needs to be obtained from external SLAM or localization nodes. |
View tf tree
xxxxxxxxxxrosrun rqt_tf_tree rqt_tf_tree
9.4. Expansion testing
Note: This case is a test version and is related to device performance. If the performance is too low, it may not start properly.
Start the underlying driver + orb_slam (virtual machine side)
xxxxxxxxxxroslaunch yahboomcar_slam test_orb_slam.launch
[bUseViewer] parameter: whether to open the visualization window of orbslam.
Start octree mapping (virtual machine side)
xxxxxxxxxxroslaunch yahboomcar_slam test_pcl_mapping.launch
[use_viewer] parameter: whether to open the visualization window of pcl_viewer.
[local_frame_id] parameter: local map coordinate system.
[global_frame_id] parameter: global map coordinate system.
After opening, the [viewer] window will pop up, as shown below
Move the camera slowly, when a picture appears, as shown below
The coordinate system needs to be scaled and rotated as shown in the figure below
Sliding wheel: zoom in or out
Press and hold scroll wheel: pan
Left mouse button: rotate
Right mouse button: zoom in or out
Slowly move the camera to build the map as shown below. After the build is completed, [Ctrl+c] closes and the pcd point cloud file is automatically saved. The path file name under this function package is [resultPointCloudFile.pcd]
pcl_viewer installation command
xxxxxxxxxxsudo apt-get install pcl-tools
View and enter the directory where the pcd file is located
xxxxxxxxxxpcl_viewer resultPointCloudFile.pcd
