Active Neural Localization

Understand

Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment.

• Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent.
• We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently.
• The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization.