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Poit cloud SLAM with Microsoft Kinect
PrjCFUMax 20  +
PrjCFUMin 10  +
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. A recently available vision sensor which has tremendous potential for autonomous robots is the Microsoft Kinect RGB-D sensor. The thesis aims at the integration of the Kinect sensor in the framework developed for the development of a point cloud base system for SLAM. '''Material:''' *Kinect sensor and libraries *A framework for multisensor SLAM *PCL2.0 library for dealing with point clouds '''Expected outcome:''' *Algorithm able to build 3D point cloud representation of the observed scene *Point clouds processing could be used to improve the accuracy of the filter as well '''Required skills or skills to be acquired:''' *Basic background in computer vision *Basic background in Kalman filtering *C++ programming under Linux man filtering *C++ programming under Linux
PrjImage Image:PointCloudKinect.jpg  +
PrjLevel Master of Science +
PrjResArea Computer Vision and Image Analysis +
PrjResTopic None +
PrjStarts 1 January 2015  +
PrjStatus Active  +
PrjStudMax 2  +
PrjStudMin 1  +
PrjTitle Point cloud SLAM with Microsoft Kinect  +
PrjTutor User:MatteoMatteucci +
PrjType Thesis +
Categories ProjectProposal  +
Modification¬†dateThis property is a special property in this wiki. 31 December 2014 15:32:32  +
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