Abstract
The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in real-time under adverse environmental conditions, and within considerably diverse and new previously unseen environments.
This thesis investigates methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, and algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality.
Keywords: SLAM, Localization, Robustness, Radar