  Egocentric spatial memory (ESM) defines a memory system with encoding,
storing, recognizing and recalling the spatial information about the
environment from an egocentric perspective. We introduce an integrated deep
neural network architecture for modeling ESM. It learns to estimate the
occupancy state of the world and progressively construct top-down 2D global
maps from egocentric views in a spatially extended environment. During the
exploration, our proposed ESM model updates belief of the global map based on
local observations using a recurrent neural network. It also augments the local
mapping with a novel external memory to encode and store latent representations
of the visited places over long-term exploration in large environments which
enables agents to perform place recognition and hence, loop closure. Our
proposed ESM network contributes in the following aspects: (1) without feature
engineering, our model predicts free space based on egocentric views
efficiently in an end-to-end manner; (2) different from other deep
learning-based mapping system, ESMN deals with continuous actions and states
which is vitally important for robotic control in real applications. In the
experiments, we demonstrate its accurate and robust global mapping capacities
in 3D virtual mazes and realistic indoor environments by comparing with several
competitive baselines.
