signal_quality_network_application

Code tested under ROS 1 Melodic

Running instructions.

This code was tested under RB1 Base complete_sim.

Source workspace

source devel/setup.bash

Run the robot simulation

roslaunch rb1_base_sim_bringup rb1_base_complete.launch

Launch the signal mapper

 roslaunch era_5g_network_signal_mapper signal_mapper.launch

Configure RVIZ to show the pcl2 by adding a pointcloud2 object and add the /semantic_pcl topic. Also adjust the size of the particle to 0.05 for better visualization.

Remote control the robot:

rosrun teleop_twist_keyboard teleop_twist_keyboard.py cmd_vel:=/robot/cmd_vel

Change colour of current pointcloud:

rosrun era_5g_network_signal_mapper colour_pub.py

Change the boundingbox of the pcl2 publish around the robot.

If you want to change the boundingbox for another robot, then under ros node signal_mapper.py modify the following variables.

* height = 0.3
* lenght = 0.3
* lamba = 0.05

lamba is the amount of points to be created along the height and lenght. It may stay at 0.05 to look like the pictures presented in this tutorial.

Change the colour filtering:

If you want the robot to ignore a particular colour (not consider it an obstacle), then under the ros node costmap_translate.py create an object of the class Colour and pass the rgb code.

Green = Colour([124, 252, 0])

Then append to ACCEPTED_COLOURS list.

To save and load the pointcloud map use pcl_ros:

This command will save the pointcloud2 into a pcd file. In has as input parameter the topic where the pointcloud2 map is published.

rosrun pcl_ros pointcloud_to_pcd input:=/semantic_pcl

To publish the pointcloud again from the pcd file under a particular frame:

rosrun pcl_ros pcd_to_pointcloud 2240726000.pcd 0.1 _frame_id:=/robot_map rosrun pcl_ros pcd_to_pointcloud 2240726000.pcd 0.1 _frame_id:=/robot_map

Configure rviz to show the pointcloud2, make sure to increase the size of the particles for visualization.

reference

From pcl & current map to new occupancy grid map:

rosrun era_5g_network_signal_mapper costmap_translate.py

Result looks like this, an occupancyGrid map is created with the data of the current map /map and the semantic pcl2. In this package, the filter of objects that should not be obstacles can be done by reading the pcl2 colour and drop the points before creating the new map.

You may use map server map saver to save the map. Later on, in the launch file for the robot, in the mapserver section, add the new map. This will allow the robot to navigate taking into consideration the newly created pcl2 obstacles.

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