Difference between revisions of "Poit cloud SLAM with Microsoft Kinect"

Revision as of 17:04, 16 April 2012

Title:	Poit cloud SLAM with Microsoft Kinect	Image:PointCloudKinect.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. 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
Tutor:	MatteoMatteucci (matteo.matteucci@polimi.it), SimoneCeriani (ceriani@elet.polimi.it)
Start:	2012/04/01
Students:	1 - 2
CFU:	10 - 20
Research Area:	Computer Vision and Image Analysis
Research Topic:	none
Level:	Bs, Ms
Type:	Thesis
Status:	Active

@@ Line 18: / Line 18: @@
 *C++ programming under Linux
-|tutor=MatteoMatteucci
+|tutor=MatteoMatteucci;SimoneCeriani
 |start=2012/04/01
 |studmin=1