Master Thesis Class-aware Pointcloud Filtering and Matching for Long-Term SLAM

Fraunhofer-Gesellschaft · Stuttgart

ThesisGerman needed: none

Still open on 1 Nov

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Key facts

Subject you study: Computer Science, Software Engineering, Mechanical Engineering, Mechatronics

Since these outdoor applications have constantly changing environments due to weather conditions, seasonal variations, new constructions, and dynamic obstacles, the robot must continuously update its reference map of the whole environment. The localization module must understand which parts of the environment are static and which are semi-static or dynamic and update the global pointcloud reference map accordingly. Furthermore, the scan matching of the live pointclouds with this sparser global pointcloud map must be improved to get good pose estimate even with many dynamic obstacles blocking the view.

Call for applications for the field of study, such as: Computer Science, Software Engineering, Mechanical Engineering, Mechatronics or similar

Since these outdoor applications have constantly changing environments due to weather conditions, seasonal variations, new constructions, and dynamic obstacles, the robot must continuously update its reference map of the whole environment. The localization module must understand which parts of the environment are static and which are semi-static or dynamic and update the global pointcloud reference map accordingly. Furthermore, the scan matching of the live pointclouds with this sparser global pointcloud map must be improved to get good pose estimate even with many dynamic obstacles blocking the view.

Call for applications for the field of study, such as: Computer Science, Software Engineering, Mechanical Engineering, Mechatronics or similar