Doctoral student position at the School of Science and Technology, Örebro University.
Project description
The research topics for this position are aimed towards truly robust, long-term, all-weather operation for self-driving vehicles and robots. More concretely, the research will be centered around modern 3D data representations, such as neural radiance fields and Gaussian splatting, and processing of state-of-the-art high-resolution radar sensors — and the combination thereof. Common sensors like lidars and cameras struggle in low-visibility conditions (dust, smoke, fog). Despite recent progress toward long-term autonomy, ensuring reliable perception in all environment conditions remains a challenge. Radar presents several advantages for robust perception: long range, instantaneous per-point speed data, and insensitivity to attenuation. However, radar comes with its own set of challenges, including noise, multipath reflections, and artifacts arising from the wide beam width. Addressing these challenges is crucial for harnessing the full potential of radar in autonomous navigation and perception.
The doctoral student will have the opportunity to use the facilities and collaborate with other researchers and industry partners at the WASP Research Arena on Public Safety as well as using field robots and sensor systems available at Örebro University.