Robocentric MoonSLAM - Revision history

MatteoMatteucci at 23:42, 21 December 2014

2014-12-21T23:42:08Z

MatteoMatteucci at 15:50, 16 April 2012

2012-04-16T15:50:30Z

MatteoMatteucci: Created page with "{{ProjectProposal |title=Robocentric implementation in the MoonSLAM framework |image=RobocentricSLAM.jpg |description=Simultaneous Localization and Mapping (SLAM) is one of th..."

2012-04-16T15:46:45Z

Created page with "{{ProjectProposal |title=Robocentric implementation in the MoonSLAM framework |image=RobocentricSLAM.jpg |description=Simultaneous Localization and Mapping (SLAM) is one of th..."

New page

{{ProjectProposal
|title=Robocentric implementation in the MoonSLAM framework
|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;SimoneCeriani
|start=2012/04/01
|studmin=1
|studmax=2
|cfumin=20
|cfumax=20
|resarea=Robotics
|restopic=none
|level=Ms
|type=Thesis
|status=Active
}}

@@ Line 26: / Line 26: @@
 |level=Ms
 |type=Thesis
-|status=Active
+|status=Closed
 }}

@@ Line 1: / Line 1: @@
 {{ProjectProposal
 |title=Robocentric implementation in the MoonSLAM framework
-|image=RobocentricSLAM.jpg
+|image=RobocentricSLAM.gif
 |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.