Spinning radar has recently become compact and accurate to be used in robotics. The sensor is resilient to dust and operates well in all weather, and has great potential to enable highly robust robotic perception systems. Previously, the sensor data has been considered hard to interpret, however due to recent advantages  the sensor data can now be used to accurately estimate odometry (sensor movement). Additionally, some work have used spinning radar for detecting when the sensor has revisited a previously seen location . However, limited work has been done exploring how to use spinning radar to create a consistent map (SLAM - Simultaneous Localization and Mapping).
Motivation and scope
This thesis aims to combine the work by Adolfsson  and Kim  and create a full SLAM system such as in . A brief implementation of this method is already in place and the focus will be on the integration of place recognition and loop closure.
Specifically, the challenge is how to use the information that a place is revisited to correct for the accumulated odometry drift. This can be addressed by a method for course registration, e.g. based on a method for robust data association . Methods for solving these problems exist, the thesis will focus on putting pieces together and performing an evaluation.
A benchmark is already integrated in the code base.
You will get to work with a sensor type that is rapidly getting an increasing amount of attention within academia and from the industry, such as Boliden, Epiroc and Volvo. You will get to use a state-of-the-art method radar odometry, which has not yet been publicly released. A successful thesis may result in an academic publication.
Good programming skills, c++.