scitos_3d_mapping¶
Tools for building 3D maps and using these maps for navigation and visualization.
Start the system¶
Start all the nodes in this repository using:
roslaunch semantic_map_launcher semantic_map.launch
Data acquisition¶
To collect sweeps, use the action server from:
cloud_merge do_sweep.py
To start the action server manually (already launched with
roslaunch semantic_map_launcher semantic_map.launch):
rosrun cloud_merge do_sweep.py
Use:
rosrun actionlib axclient.py /do_sweep
This action server takes as input a string, with the following values
defined: “complete”, “medium”, “short”, “shortest”. Internally the
action server from scitos_ptu called
ptu_action_server_metric_map.py is used, so make sure that is
running.
The behavior is the following: * If sweep type is complete, the
sweep is started with parameters -160 20 160 -30 30 30 -> 51
positions * If sweep type is medium, the sweep is started with
parameters -160 20 160 -30 30 -30 -> 17 positions * If sweep type
is short, the sweep is started with parameters
-160 40 160 -30 30 -30 -> 9 positions * If sweep type is
shortest, the sweep is started with parameters
-160 60 140 -30 30 -30 -> 6 positions (there might be blank areas
with this sweep type, depending on the environment).
Calibrate sweep poses¶
Once a number of sweeps of type “complete” have been collected, you can run the calibration routine which will compute the registration transformations for the 51 poses. Afterwards, you can execute sweeps of any type (from the types defined above) and the correct transformations will be loaded so that the sweeps are registered.
To start the action server manually (already launched with
roslaunch semantic_map_launcher semantic_map.launch):
rosrun calibrate_sweeps calibrate_sweep_as
Use:
rosrun actionlib axclient.py /calibrate_sweeps
(Here you have to specify the minimum and maximum number of sweeps to use for the optimization. To get good registration results you should have collected > 5 sweeps. Note that only sweeps of type “complete” are used here, all others are ignored).
Once the calibration has been executed, the parameters are saved in
~/.ros/semanticMap/ from where they are loaded whenever needed. All
sweeps recorded up to this point are automatically corrected using the
registered sweeps.
Meta-Rooms¶
The Meta-Rooms are created by the semantic_map semantic_map_node. To
start, run:
roslaunch semantic_map semantic_map.launch
For more information check out the semantic_map package.
The dynamic clusters are published on the
/local_metric_map/dynamic_clusters topic and the Meta-Rooms are
published on the /local_metric_map/metaroom topic.
Reinitialize the Meta-Rooms¶
After the calibration you can re-initialize the metarooms (in general a good idea, as the registration between the sweeps should be better now that the poses have been calibrated).
rosservice call /local_metric_map/ClearMetaroomService "waypoint_id: - 'WayPointXYZ' initialize: true"
Set the argument initialize to true and provide all the waypoints
for which you want to re-initialize the metarooms in the waypoint_id
list.
Access invidual dynamic clusters¶
The package object_manager allows access to individual dynamic
clusters, via a number of services. To start use:
rosrun object_manager object_manager_node
For more information check out the object_manager package.
semantic_map_publisher¶
The package semantic_map_publisher provides a number of services for
accessing previously collected data which is stored on the disk. To
start use:
rosrun semantic_map_publisher semantic_map_publisher
For more information check out the semantic_map_publisher package.
Accessing saved data¶
The package metaroom_xml_parser provides a number of utilities for
reading previously saved sweep data. These include utilities for
accessing:
- merged point clouds
- individual point clouds
- dynamic clusters
- labelled data
- sweep xml files.
Check out the metaroom_xml_parser package for more information.
Original page: https://github.com/strands-project/strands_3d_mapping/blob/hydro-devel/README.md