DEQ-MCL: Discrete-Event Queue-based Monte-Carlo Localization
This work addresses self-localization for robots in indoor settings, presenting an incremental approach based on biological inspiration.
The paper tackles robot self-localization by proposing DEQ-MCL, a method based on hippocampal discrete event queues, which estimates posterior distributions of past, present, and future states to smooth past states and weight future feasibility, showing promise for improved performance in indoor environments.
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.