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

Call for applications for the field of study, such as: automation technology, electrical engineering, computer science, cybernetics, mechanical engineering, mathematics, mechatronics, physics, control engineering, software design, software engineering, technical computer science, surveying engineering or similar.

In the research group Navigation Mobile Robots, we develop autonomous robots for a variety of outdoor applications, such as agriculture, forestry and logistics. The focus is on the development of an autonomous outdoor navigation solution as well as the hardware of the robots.

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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.