# Multiparticle Kalman filter for object localization in symmetric environments

arXiv: https://arxiv.org/abs/2303.07897 · HTML: https://mlsanity.com/paper/2303.07897/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks](https://mlsanity.com/paper/1301.3853/) (2013)
- [Towards Differentiable Resampling](https://mlsanity.com/paper/2004.11938/) (2020)
- [Faster Matrix Multiplication via Asymmetric Hashing](https://mlsanity.com/paper/2210.10173/) (2022)
- [Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches](https://mlsanity.com/paper/1308.2443/) (2013)
- [Open-Source LiDAR Time Synchronization System by Mimicking GNSS-clock](https://mlsanity.com/paper/2107.02625/) (2021)

## Built on by (most cited first, 2 of 2)

- [Memory-efficient particle filter recurrent neural network for object localization](https://mlsanity.com/paper/2310.01595/) (2023)
- [Multi-AGV Path Planning Method via Reinforcement Learning and Particle Filters](https://mlsanity.com/paper/2403.18236/) (2024)

## Abstract (arXiv)

This study considers the object localization problem and proposes a novel multiparticle Kalman filter to solve it in complex and symmetric environments. Two well-known classes of filtering algorithms to solve the localization problem are Kalman filter-based methods and particle filter-based methods. We consider these classes, demonstrate their complementary properties, and propose a novel filtering algorithm that takes the best from two classes. We evaluate the multiparticle Kalman filter in symmetric and noisy environments. Such environments are especially challenging for both classes of classical methods. We compare the proposed approach with the particle filter since only this method is feasible if the initial state is unknown. In the considered challenging environments, our method outperforms the particle filter in terms of both localization error and runtime.