INTERNSHIP DETAILS

Studien-/Abschlussarbeit: Dynamic Obstacle Prediction for Autonomous Outdoor Navigation

CompanyFraunhofer-Gesellschaft
LocationStuttgart
Work ModeOn Site
PostedJuly 30, 2026
Internship Information
Core Responsibilities
Develop autonomous navigation solutions for mobile robots in dynamic outdoor environments. Implement obstacle prediction and classification using LiDAR and camera data integrated into ROS2 costmaps.
Internship Type
full time
Company Size
295
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
Fraunhofer IGD is the international leading institute for applied research in visual computing. Visual computing is image- and model-based information technology and includes computer graphics and computer vision, as well as virtual and augmented reality. In simple terms, the Fraunhofer researchers in Darmstadt, Rostock, and Kiel are turning information into images and extracting information from images. In cooperation with its partners, technical solutions and market-relevant products are created. Prototypes and integrated solutions are developed in accordance with customized requirements. In doing so, Fraunhofer IGD places users at the forefront, providing them with technical solutions to facilitate computer work and make it more efficient. Owing to its numerous innovations, Fraunhofer IGD raises man-machine interaction to a new level. Man is able to work in a more result-oriented and effective way by means of the computer and visual computing developments.
About the Role

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

 

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

 

 

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Autonomous navigation in dynamic environments and higher speeds create challenges to typical mobile robots. To ensure safe and efficient path planning the robot must predict the future movement of dynamic obstacles and integrate that into its world understanding. This includes classifying the obstacle into typical dynamic obstacles like pedestrians, bikes, and vehicles based on LiDAR and/or camera data, predicting a realistic and/or conservative movement corridor and integrate this information efficiently into ROS2 costmaps. An important integration requirement lies in the compatibility with the ROS2 Nav2 Stack, which is widely used in the state of art of mobile robotics navigation.

Key Skills
Autonomous NavigationROS2Nav2 StackLiDARComputer VisionPath PlanningObstacle PredictionRoboticsControl EngineeringSoftware EngineeringAutomation TechnologyMechatronicsSensor Data Processing
Categories
EngineeringTechnologyScience & ResearchSoftwareAgriculture