Fraunhofer

Masterarbeit - Semantisches Pointcloud Filtering und Matching für Long-Term SLAM

Fraunhofer

Stuttgart · Posted Aug 12

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Master Thesis: Class-aware Pointcloud Filtering and Matching for Long-Term SLAM. Develop a global pointcloud mapping pipeline consisting of a pointcloud filtering approach (utilizing camera-based classifications) and a pointcloud matching algorithm to update a sparse global map for outdoor mobile robotics. Implement inside the ROS2/C++ navigation stack and evaluate performance in simulation and real-world scenarios with Fraunhofer IPA’s CURT robots in Stuttgart. Requires valid enrollment at a German university and a background in Computer Science, Software Engineering, Mechatronics, or related fields; programming experience; ROS experience is a plus; fluent in English or German.

What they are looking for

Ros Ros2 Cpp Pointcloud

Details

Work type
Onsite
Compensation
Paid
Remote eligible
No

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