title: Egocentric Spatial Memory Network
abstract: Inspired by neurophysiological discoveries of navigation cells in the mammalian
  brain, we introduce the first deep neural network architecture for modeling Egocentric
  Spatial Memory (ESM). It learns to estimate the pose of the agent and
  progressively construct top-down 2D global maps from egocentric views in a spatially
  extended environment. During the exploration, our proposed ESM network
  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 based on
  their corresponding locations in the egocentric coordinate. This enables the agents
  to perform loop closure and mapping correction. This work contributes in the
  following aspects: first, our proposed ESM network provides an accurate mapping
  ability which is vitally important for embodied agents to navigate to goal locations.
  In the experiments, we demonstrate the functionalities of the ESM network in
  random walks in complicated 3D mazes by comparing with several competitive
  baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
  algorithms. Secondly, we faithfully hypothesize the functionality and the working
  mechanism of navigation cells in the brain. Comprehensive analysis of our model
  suggests the essential role of individual modules in our proposed architecture and
  demonstrates efficiency of communications among these modules. We hope this
  work would advance research in the collaboration and communications over both
  fields of computer science and computational neuroscience.
authors: Mengmi Zhang Keng Teck Ma Joo Hwee Lim Shih-Cheng Yen Qi Zhao Jiashi Feng
hIndex: Jiashi Feng 76