  Spatial cognition in hippocampal formation is posited to play a crucial role
in the development of self-localization techniques for robots. In this paper,
we propose a self-localization approach, DEQ-MCL, based on the discrete event
queue hypothesis associated with phase precession within the hippocampal
formation. Our method effectively estimates the posterior distribution of
states, encompassing both past, present, and future states that are organized
as a queue. This approach enables the smoothing of the posterior distribution
of past states using current observations and the weighting of the joint
distribution by considering the feasibility of future states. Our findings
indicate that the proposed method holds promise for augmenting
self-localization performance in indoor environments.
