Poit cloud SLAM with Microsoft Kinect - Revision history

AndreaBonarini at 15:32, 31 December 2014

2014-12-31T15:32:32Z

MatteoMatteucci at 23:40, 21 December 2014

2014-12-21T23:40:04Z

MatteoMatteucci at 16:04, 16 April 2012

2012-04-16T16:04:15Z

MatteoMatteucci: Created page with "{{ProjectProposal |title=Poit cloud SLAM with Microsoft Kinect |image=PointCloudKinect.jpg |description=Simultaneous Localization and Mapping (SLAM) is one of the basic functi..."

2012-04-16T15:23:13Z

Created page with "{{ProjectProposal |title=Poit cloud SLAM with Microsoft Kinect |image=PointCloudKinect.jpg |description=Simultaneous Localization and Mapping (SLAM) is one of the basic functi..."

New page

{{ProjectProposal
|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
|start=2012/04/01
|studmin=1
|studmax=2
|cfumin=10
|cfumax=20
|resarea=Computer Vision and Image Analysis
|restopic=none
|level=Bs;Ms
|type=Thesis
|status=Active
}}

@@ Line 1: / Line 1: @@
 {{ProjectProposal
-|title=Poit cloud SLAM with Microsoft Kinect
+|title=Point 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.

@@ Line 18: / Line 18: @@
 *C++ programming under Linux
-|tutor=MatteoMatteucci;SimoneCeriani
+|tutor=MatteoMatteucci
-|start=2012/04/01
+|start=2015/01/01
 |studmin=1
 |studmax=2
@@ Line 26: / Line 26: @@
 |resarea=Computer Vision and Image Analysis
 |restopic=none
-|level=Bs;Ms
+|level=Ms
 |type=Thesis
 |status=Active
 }}