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.
@article{arxiv.1802.06857,
title = {Global Pose Estimation with an Attention-based Recurrent Network},
author = {Emilio Parisotto and Devendra Singh Chaplot and Jian Zhang and Ruslan Salakhutdinov},
journal= {arXiv preprint arXiv:1802.06857},
year = {2018}
}