Application of Sequential Quasi-Monte Carlo to Autonomous Positioning

TL;DR

This paper explores Sequential Quasi-Monte Carlo (SQMC), a faster-converging variant of particle filters, demonstrating its improved performance in autonomous positioning tasks compared to traditional methods.

Contribution

It introduces SQMC as an enhancement over standard SMC, applying low-discrepancy sequences to improve convergence rates in real-time positioning applications.

Findings

SQMC converges faster than traditional SMC.

SQMC outperforms SMC in autonomous positioning scenarios.

Empirical results show improved accuracy and efficiency.

Abstract

Sequential Monte Carlo algorithms (also known as particle filters) are popular methods to approximate filtering (and related) distributions of state-space models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater performance of SQMC on autonomous positioning problems.