SLAM
Chapter description
In this tutorial, we will walk you through the map creation process and discuss the SLAM algorithm, which consists of two parts: mapping and localization. The gmapping
package will be used for mapping, the amcl
package will be used to locate the robot on the map. Mapping is usually a necessary element to create more advanced autonomous driving systems. In addition, thanks to the knowledge of the map, we can complete the location of the robot. This is because we can compare the currently received sensor data and match it to the map. To do this, you will need to collect data from the RPLiDAR on board your ROSbot.
You can run this tutorial on:
Repository containing the final effect after doing all the ROS Tutorials you can find here
Introduction
SLAM (simultaneous localization and mapping) is a technique for creating a map of environment and determining robot position at the same time. It is widely used in robotics. While moving, current measurements and localization are changing, in order to create map it is necessary to merge measurements from previous positions.
For definition of SLAM problem we use given values (1,2) and expected values (3,4):
- Robot control
- Observations
- Robot trajectory