Simultaneous Localization and Mapping (SLAM)
In the field of robotics, Simultaneous Localization and Mapping (SLAM) is the problem of constructing and constantly updating a map of an environment that is unknown to the robot while at the same time keeping track of the robot’s location in that map.
In this project, I developed several algorithms to implement the SLAM technique for a mobile robot in an indoor environment. For this, I used odometry, inertial and range measurements that had been previously collected using the robot. The LIDAR used to connect these measurements is Hokuyo UTM-30LX. The sensors the robot used to collect these measurements include wheel encoders, a Light Detection and Ranging sensor (LIDAR), and an Inertial Measurement Unit (IMU).
The project has two main parts. For the first part, I estimated the map and robot position using the dead-reckoning technique. For the second part, I implemented a Particle Filter (PF) with systematic re-sampling to implement SLAM and improve the results obtained in the previous part.
For this project I used Python. I avoided using any SLAM libraries, so I can confidently say that I now understand how the algorithms work through and through!
Check out a few videos for different environments the robot visited, and click on the button to the project repository to read the report!