Heterogeneous sensor fusion and calibration

Robotic autonomy is a rapidly evolving field, with sensor fusion playing a pivotal role in enabling robots to make informed real-time decisions, navigate in complex environments, and ensure safe and efficient operations. Sensor fusion involves the data integration from diverse sensors, such as cameras, lidars, radars, and GPS, to create a unified and comprehensive understanding of the robot's surroundings or location in the environment. To achieve accurate and robust sensor fusion, it is imperative to address the calibration of these sensor systems. 

We aim to develop algorithms and methodologies to determine spatial relations and synchronize these varied sensor systems and, finally, fuse the incoming information to estimate the robot's position in the environment.

Object manipulation and human-robot collaboration

Manipulating objects in the environment is fundamental in many industrial and commercial applications, from manufacturing assembly lines to warehouse management. Automating this process using robotic arms not only enhances efficiency but also reduces human errors and can operate in environments that might be hazardous to humans. The goal of this topic is to design, simulate and implement a system where a robotic arm can identify, pick, and place objects in specified locations. Additionally, including the human in the process so as to execute complex tasks jointly often includes detecting the human and inferring their intentions.

Specific thesis topics can focus on perception, motion planning, motion control and integration of the complete pipeline. As well as human detection, pose esimation and intention esimation using the Bayesian theory of mind. The challenge is to ensure that robotic arms can autonomously and accurately perform this task with or without the human in the loop, adapting to different objects and environments.

3D perception, localization and mapping