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.

我们提出了一种基于海马结构内部相位前进的离散事件队列假设的自定位方法 DEQ-MCL，该方法有效地估计了包括过去、现在和未来状态的后验分布，通过考虑未来状态的可行性来平滑过去状态的后验分布，并通过当前观察来加权联合分布。研究结果表明，该方法在室内环境中增强了自定位性能。