Global Pose Estimation with an Attention-based Recurrent Network
This work addresses SLAM for agents in real-world applications, but it appears incremental as it builds on existing neural network approaches.
The authors tackled the problem of simultaneous localization and mapping (SLAM) in unknown environments by proposing a differentiable architecture called Neural Graph Optimizer, which includes novel modules for pose selection and graph optimization, and demonstrated its effectiveness on simulated 2D and 3D environments.
The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.