Open Thesis

Ongoing Thesis

Master's Theses

Attentive observation using intensity-assisted segmentation for SLAM in a dynamic environment

Keywords:
SLAM, ROS, Deep Learning, Segmentation

Description

Attentive observation using intensity-assisted segmentation for SLAM in a dynamic environment.

 

Supervisor:

Solid-State LiDAR and Stereo-Camera based SLAM for unstructured planetary-like environments

Keywords:
Solid-State LiDAR; Stereo-Camera; SLAM

Description

New developments in solid-state LiDAR technology open the possibility of integrating range sensors in possible space-qualifiable perception setups, thanks to mechanical designs with reduced moveable parts. Thereby, the development of a hybrid stereo-camera/LiDAR sensor setup might overcome disadvantages each technology comes with, such as limit range for stereo camera setups or the minimum range Lidars need. This thesis investigates such a new solid-state Lidar's possibilities by incorporating it along with a stereo camera setup and an IMU sensor into a SLAM system. Foreseen activities might include, but are not limited to, the design and construction of a portable/handhold sensor setup for recording and testing in planetary-like environments, extrinsic calibration of the sensors, integration into a software pipeline, development of a ROS interface, and preliminary mapping tests.

Supervisor:

Mojtaba Karimi - (German Space Agency (DLR))

Deep Predictive Attention Controller for LiDAR-Inertial localization and mapping

Keywords:
SLAM, Sensor Fusion, Deep Learning

Description

The multidimensional sensory data is computationally expensive for localization algorithms in autonomous navigation for drones. Research shows that not all sensory data are equivalently important during the entire process of SLAM to perform a reliable output. The attention control scheme is one of the effective ways to filter out the highly valuable sensory data for such a system. The predictive attention model, for instance, can help us to improve the result of the sensor fusion algorithms by concentrating on the most valuable sensory data based on the dynamic of the vehicle motion or the semantic understanding of the environment. The aim of this work is to investigate the state-of-the-art attention control models that can be adapted for the multidimensional sensory data acquisition system and compare them from different modalities. 

Prerequisites

- Strong background in Python and C++ programming

- Solid background in robot control theory

- Be familiar with deep learning frameworks (Tensorflow)

- Be familiar with the robot operating system (ROS)

Contact

leox.karimi@tum.de

Supervisor:

Research Internships (Forschungspraxis)

Adaptive LiDAR data update rate control based on motion estimation

Keywords:
SLAM, Sensor Fusion, ROS

Description

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Supervisor: