Robocentric MoonSLAM

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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.


  • A framework for multisensor SLAM using the world centric approach
  • Papers and report about robocentric slam

Expected outcome:

  • a fully functional robocentric version of the MoonSLAM framework

Required skills or skills to be acquired:

  • Basic background in computer vision
  • Background in Kalman filtering
  • C++ programming under Linux
Tutor: MatteoMatteucci (, SimoneCeriani (
Start: 2012/04/01
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: none
Level: Ms
Type: Thesis
Status: Closed