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

arXiv: https://arxiv.org/abs/2404.13973 · HTML: https://mlsanity.com/paper/2404.13973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping](https://mlsanity.com/paper/1803.03481/) (2018)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)

## Abstract (arXiv)

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.