Difference between revisions of "Robocentric MoonSLAM"
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|title=Robocentric implementation in the MoonSLAM framework | |title=Robocentric implementation in the MoonSLAM framework | ||
| − | |image=RobocentricSLAM. | + | |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. | |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. | ||
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|level=Ms | |level=Ms | ||
|type=Thesis | |type=Thesis | ||
| − | |status= | + | |status=Closed |
}} | }} | ||
Latest revision as of 00:42, 22 December 2014
| Title: | Robocentric implementation in the MoonSLAM framework |  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.
Material
Expected outcome:
Required skills or skills to be acquired:
| |
| Tutor: | MatteoMatteucci (matteo.matteucci@polimi.it), SimoneCeriani (ceriani@elet.polimi.it) | |
| Start: | 2012/04/01 | |
| Students: | 1 - 2 | |
| CFU: | 20 - 20 | |
| Research Area: | Robotics | |
| Research Topic: | none | |
| Level: | Ms | |
| Type: | Thesis | |
| Status: | Closed |
