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Robocentric MoonSLAM
PrjCFUMax 20  +
PrjCFUMin 20  +
PrjDescription Simultaneous Localization and Mapping (SLA … 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 man filtering *C++ programming under Linux
PrjImage Image:RobocentricSLAM.gif  +
PrjLevel Master of Science +
PrjResArea Robotics +
PrjResTopic None +
PrjStarts 1 April 2012  +
PrjStatus Closed  +
PrjStudMax 2  +
PrjStudMin 1  +
PrjTitle Robocentric implementation in the MoonSLAM framework  +
PrjTutor User:MatteoMatteucci + , User:SimoneCeriani +
PrjType Thesis +
Categories ProjectProposal  +
Modification¬†dateThis property is a special property in this wiki. 21 December 2014 23:42:08  +
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