Difference between revisions of "Robocentric MoonSLAM"

Revision as of 16:46, 16 April 2012

Title:	Robocentric implementation in the MoonSLAM framework	300px Image:RobocentricSLAM.jpg
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. Material 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 (matteo.matteucci@polimi.it), SimoneCeriani (ceriani@elet.polimi.it)
Start:	2012/04/01
Students:	1 - 2
CFU:	20 - 20
Research Area:	Robotics
Research Topic:	none
Level:	Ms
Type:	Thesis
Status:	Active