At the Neustrelitz site, we are researching new technologies for reliable navigation in the maritime sector. The Multi Sensor Systems Group conducts research on sensor fusion processes to provide reliable and precise navigation and perception information for vessels and other marine platforms. These are the basis for gradually increasing the degree of automation of ships up to fully autonomous ships.

Together with partners in the AutonomousSOW and DigitalSOW projects, we are building a testbed for autonomous inland vessels on the Spree and Oder rivers (SOW from its German acronym Spree-Oder Wasserstrasse), which go through the Berlin metropolitan area. In this testbed, the infrastructure for the development and practical testing of autonomous inland vessels will be established. Thus, the first test vehicles (ships) will be built and tested there.

Your work consists on fusing data from 3D-LiDARs, cameras and navigation sensors (i.e., GNSS and inertial sensors) to provide robust odometry and mapping information within the context of inland waterway and maritime scenarios. Particularly, you are expected to work on the field of probabilistic Simultaneous Localization and Mapping (SLAM) estimators. Your algorithms shall deal with the typical challenges of water scenarios, such as the sparsity of visual features, low lighting conditions and water reflections.

Your readiness for extended security clearance (Ü1) is required for the position.
In addition to a highly motivated team, excellent experimental opportunities and an exciting task, we offer a very attractive working environment. This includes, in particular, flexible working hours and individual arrangements for mobile working.

Your tasks include:

• integration of LiDAR and camera information in our software framework for position, navigation and time determination.
• research and development of methods for high definition mapping in inland waterway applications. Implementation of real-time perception solutions using ROS/ROS2 for validation on experimental vessels in real-world applications.
• design, train and deploy Deep Learning models based on LiDAR and/or camera data to enable various perception tasks.
• documentation of results in the form of technical reports, conference papers and contributions to scientific journals.
• participation in the preparation of project proposals.
• scientific and administrative participation in measurement campaigns as well as preparation and follow-up work