A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

TL;DR

This paper introduces a low-complexity, real-time capable SLAM filter for mmWave radio environments that efficiently combines EK-PMBM filtering with TOMB/P approximation, maintaining high accuracy with reduced computational cost.

Contribution

It presents a novel EK-PMBM filter for radio SLAM that reduces computational complexity using Gaussian approximation and TOMB/P, enabling real-time performance.

Findings

Significantly reduces computational cost compared to existing methods.

Maintains high accuracy in mapping and localization.

Supports multiple landmark types and data association hypotheses.

Abstract

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.