| 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 +
|
