Global localization lies at the heart of autonomous navigation and Simultaneous Localization and Mapping (SLAM). The appearance-based approach has been successful, but still faces many open challenges in environments where visual conditions vary significantly over time. In this paper, we propose an integrated solution to leverage object-level dense semantics and spatial understanding of the environment for global localization. Our approach models an environment with 3D dense semantics, semantic graph and their topology. This object-level representation is then used for place recognition via semantic object association, followed by 6-DoF pose estimation by the semantic-level point alignment. Extensive experiments show that our approach can achieve robust global localization under extreme appearance changes. It is also capable of coping with other challenging scenarios, such as dynamic environments and incomplete query observations.
|Title of host publication||2019 International Conference on Robotics and Automation (ICRA)|
|Number of pages||7|
|Publication status||Published - 12 Aug 2019|
|Name||International Conference on Robotics and Automation (ICRA)|
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering