Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization

In mobile robotics, particularly in autonomous driving, localization is one of the key challenges for navigation and planning. For safe operation in the open world where vulnerable participants are present, precise and guaranteed safe localization is required. While current classical fusion approaches are safe due to provably bounded closed-form formulation, their situation-adaptivity is limited. In contrast, data-driven approaches are situation-adaptive based on the underlying training data but unbounded and unsafe. In our work, we propose a novel data-driven but provably bounded sensor fusion and apply it to mobile robotic localization. In extensive experiments using an autonomous driving test vehicle, we show that our fusion method outperforms other safe fusion approaches.