Introduction
Robust ego-vehicle localization is a core requirement for autonomous driving. In real-world operation, performance can degrade due to adverse weather and non-ideal sensor behavior, such as dropouts, partial failures, miscalibration, or temporal misalignment. This thesis topic focuses on dataset-driven, learning-based approaches to improve localization robustness in multi-sensor settings, with systematic experimental evaluation.
Goals
This thesis aims to explore data-driven methods for robust ego-vehicle localization in a multi-sensor context. The specific objective, dataset(s), and sensor configuration will be defined case by case based on the student’s interests and background, and refined after an initial introductory meeting. Validation will be carried out through experiments on datasets and comparison against appropriate baselines using standard localization/odometry metrics.
Requirements:
- Knowledge of Matlab or Python;
- Basic knowledge of control theory, vehicle dynamics and machine learning are a plus (can be learned during the project).
Contact
Davide Possenti: davide.possenti@polimi.it
Stefano Arrigoni: stefano.arrigoni@polimi.it
For inquiries and further information, please email the first author, copying the other authors in CC