Localisation

Localisation and mapping is a key requirement of self-driving cars and other autonomous robots. It is well studied in the forms of angulation and lateration for known anchor positions and, more generally, simultaneous localisation and mapping (SLAM) and structure from motion (SfM) for unknown anchor positions.

My research in this area aims to abstract the key essence of these problems to reveal fundamental performance limits and connections to a priori unrelated fields. In addition, I investigate novel regularisers and priors that can be used in practical localisation and mapping algorithms.

Related publications

Journal papers

• M. Pacholska, F. Dümbgen and A. Scholefield. Relax and Recover: Guaranteed Range-Only Continuous Localization; IEEE Robotics and Automation Letters. vol. 5, no. 2, pp. 2248 - 2255, Apr 2020.
• A. Scholefield A. Ghasemi and M. Vetterli. Bound and conquer: Improving triangulation by enforcing consistency; IEEE Transactions on Pattern Analysis and Machine Intelligence; 2019.

Conference papers

• F. Dümbgen et. al. Multi-Modal Probabilistic Indoor Localization on a Smartphone; IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2019.
• G. Baechler, F. Dümbgen, G. Elhami, M. Krekovic, R. Scheibler, A. Scholefield, M. Vetterli. Combining Range and Direction for Improved Localization; IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
• A. Ghasemi, A. Scholefield, M. Vetterli. Shape: Linear-time camera pose estimation with quadratic error-decay; IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
• A. Ghasemi, A. Scholefield, M. Vetterli. On the accuracy of point localisation in a circular camera-array; IEEE International Conference on Image Processing (ICIP), 2015.