|Title:||Robocentric implementation in the MoonSLAM framework|| |
|Description:||Simultaneous Localization and Mapping (SLAM) is one of the basic functionalities required from an autonomous robot. In the past we have developed a framework for building SLAM algorithm based on the use of the Extended Kalman Filter and vision sensors. The actual implementation of the EKF SLAM in the framework developed uses a world-centric approach, but from the literature it is known that a robocentric approach can provide higher performances on small maps. We would like to have both implementation to compare the results in two scenarios: pure visual odometry, conditional independent submapping.
Required skills or skills to be acquired:
|Tutor:||MatteoMatteucci (email@example.com), SimoneCeriani (firstname.lastname@example.org)|
|Students:||1 - 2|
|CFU:||20 - 20|