Towards general end-to-end sensor fusion for robot localization: implementa-tion of visual-inertial-wheel odometry

Abstract

This thesis aims at generalizing a state-of-the-art end-to-end approach for robot localization in GNSS (GPS) deprived environments using a monocular camera, inertial sensors, and wheels encoder. The pipeline is trained and tested for autonomous vehicles, but the work aims to develop multimodal robot localization and observe how the method can be generalized. This thesis starts with an overview of the localization methods, structured to highlight the challenge of localization and sensor fusion, followed by a description of the state-of-the-art learning-based methods. Then, the data analysis and preprocessing are explained in the methods, as well as the structure of the pipeline, with a detailed analysis of its building blocks. Later, the results are shown and discussed, providing a comparison with the existing methods. To conclude the thesis, some observations about the presented methods and their future developments will be presented. This work has a solid industrial relevance since robustness in localization is an open problem and requires tailored engineering efforts, while having a strong research interest since it develops and expands the state of the art at the intersection between robotics and artificial intelligence.