SeanNet: Semantic Understanding Network for Localization Under Object Dynamics

Understand

We aim for domestic robots to perform long-term indoor service.

• Under the object-level scene dynamics induced by daily human activities, a robot needs to robustly localize itself in the environment subject to scene uncertainties.
• Previous works have addressed visual-based localization in static environments, yet the object-level scene dynamics challenge existing methods for the long-term deployment of the robot.
• This paper proposes a SEmantic understANding Network (SeanNet) architecture that enables an effective learning process with coupled visual and semantic inputs.