Learning methods for robust localization - Chen, C (2020)
Location awareness is a fundamental need for intelligent systems, such as self-driving vehicles, delivery drones, and mobile devices. Given their on-board sensors (e.g. camera, inertial sensor and LIDAR), previous researchers have developed a variety of localization systems, by building hand-crafted models and algorithms. Under ideal conditions, these sensors and models are able to accurately estimate system states without time bound. However, in real-world environments, many issues such as imperfect sensor measurements, inaccurate system modelling, complex environmental dynamics and unrealistic constraints, degrade the accuracy and reliability of localization systems. Therefore, this thesis aims to leverage machine learning approaches to overcome the intrinsic problems of the human-designed localization models.