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SLAM

Simultaneous localization and Mapping

what is slam

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken-and-egg problem there are several algorithms known for solving it, at least approximately, in tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality (Wikipedia).

SLAm with bveeta mini 

There are two methods used by Bveeta Mini to do SLAM.

  1. Hector SLAM

  2. Gmapping SLAM

For more information details on the above, please visit https://ros.org

1. bveeta mini with hector slam

To run Hector SLAM on Bveeta Mini, please follow steps below:

    1. Open New 4 terminals

    2. First terminal, enter:

   

    3. Second terminal enter:

   

    4. Third terminal enter:

   

    5. Fourth terminal enter:

   

    6. In the Rviz window, open the configure files name in Home directory >"bveeta_rviz.rviz"

   

 

    7. The Rviz layout should appear like Figure below:

   

 

 

   

 

   

    8. Set your robot angular and linear speed to low by pressing X or Z key few times. The reason is when doing          SLAM, it requires high capacity of the SBC RAM which will slow down the calculation and processing                  power.

    9. Runs your robot until it creates enough map features for your requirement.

    10. Once the map is satisfied, save the map created so that it can be use for navigation later. To save the                  map, open new terminal, and enter the following:

 

Warning!

When doing mapping with Hector SLAM through the remote Desktop connection, you will face an issue where the map generated is very bad. This is due to the lack capacity of Graphical processor Memory and limited resolutions of Lidar. Please consider connecting your Bveeta Mini through the ROS MASTER so it can perform well for Hector SLAM.

Navigation
Button

roscore

roslaunch hectormapping bveeta_hector_slam.launch

rosrun rviz rviz

rosrun teleop_twist_keyboard teleop_twist_keyboard.py

rosrun map_server map_saver -f myMap

2. bveeta mini with Gmapping slam

To run Hector SLAM on Bveeta Mini, please follow steps below:

    1. Open new 4 terminals

    2. First terminal, enter roscore

 

    

    3. Second terminal enter:

    4. Third terminal enter

    5. 4th terminal enter

    6. In the Rviz window, open the config files name in Home directory >  “bveeta_rviz.rviz”

 

 

 

 

 

 

 

 

 

 

     7. The Rviz layout should appear like Figure below:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

    8. Set your robot angular and linear speed to low by pressing X or Z key few times. The reason          is when doing SLAM, it requires high capacity of the SBC RAM which will slow down the                calculation and processing power.

    9. Runs your robot until it creates enough map features for your requirement.

  10. Once the map is satisfied, save the map created so that it can be use for navigation later.             To save the map, open new terminal, and enter the following:

Choose
Navigation

roscore

rosrun map_server map_saver -f myMap

rosrun rviz rviz

rosrun teleop_twist_keyboard teleop_twist_keyboard.py

roslaunch gmapping bveeta gmapping.launch

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