DEQ-MCL: Discrete-Event Queue-based Monte-Carlo Localization

TL;DR

DEQ-MCL introduces a hippocampal-inspired queue-based approach for robot self-localization, effectively estimating comprehensive state distributions and improving indoor localization accuracy.

Contribution

The paper presents a novel discrete event queue-based Monte-Carlo localization method inspired by hippocampal phase precession, enhancing state estimation in robotic localization.

Findings

Improved localization accuracy in indoor environments.

Effective smoothing of past state estimates using current observations.

Potential for hippocampal-inspired cognitive mapping.

Abstract

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.