Master Thesis - Multi-session global traversability Mapping for Autonomous Outdoor Navigation

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In the Professional Service Robots - Outdoor research group we develop autonomous, mobile 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.

For mobile robots operating in outdoor, unstructured environments with unknown terrain conditions, maintaining a consistent, global traversability map is essential. Such a map allows the robot to plan long-range, environment-aware routes and avoid obstacles effectively over extended distances.

For this purpose, it is essential to develop a robust mapping algorithm, that allows for continuous, online map growth during exploration and mapping operations. The map architecture must be optimized for memory efficiency, and it must incorporate effectively localization information and uncertainty, allowing for loop-closure corrections to reposition and align all maps generated throughout the robot’s trajectory.

Additionally, in large and dynamic agricultural environments, it is often impractical to completely map the environment in a single session. Therefore, the mapping architecture needs to allow for multi-session mapping, where the robot incrementally builds and corrects its map over several missions. It must ensure accurate integration of newly explored areas and continuously update traversed regions, to correct previous mapping errors and adapt to environmental changes.

Hier sorgen Sie für Veränderung

In this thesis, you will design the algorithm to perform long-term, multi-session traversability mapping of static environments. You will evaluate different mapping configurations, focusing on submap-based approaches, which divide the environment into smaller, independent submaps. In particular, you will evaluate how to relate, update and merge these maps along the robot’s trajectory to support multi-session mapping. You will also ensure that the mapping architecture can support loop closure corrections detected by an external localization module.

You will then implement your solution inside our ROS2, C++ navigation stack and integrate it with existing components such as the LiDAR-Inertial localization module. Its performance will then be tested both in simulation and in real-world scenarios with our mobile CURT robots.

Hiermit bringen Sie sich ein

• Student enrolled at a German university/Hochschule
• Background in Computer Science, Software Engineering, Mechatronics or similar
• Programming knowledge and experience with C/C++
• Experience with ROS is a plus
• Analytical mindset
• Enthusiasm for mobile robotics
• Fluent in English or German

Was wir für Sie bereithalten

• Cutting-edge technology in the field of outdoor mobile robotics
• Hands on with our robots in Stuttgart
• Take on responsibility and freedom to implement your own ideas
• Work with the best students in their discipline
• Familiar atmosphere including Cake Thursday

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Bereit für Veränderung? Dann bewerben Sie sich jetzt, und machen Sie einen Unterschied! Nach Eingang Ihrer Online-Bewerbung erhalten Sie eine automatische Empfangsbestätigung. Dann melden wir uns schnellstmöglich und sagen Ihnen, wie es weitergeht.

Ms. Jennifer Leppich

Recruiting

+49 711 970-1415

[email protected]

Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA

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