Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization

Understand

Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics.

• We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN).
• The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter.
• Since the DMN architecture is end-to-end differentiable, we can jointly learn the map representation and localization using gradient descent.