Dirichlet process approach for radio-based simultaneous localization and mapping

TL;DR

This paper introduces a Dirichlet process-based radio SLAM method leveraging 5G mmWave signals to accurately localize vehicles and map reflectors, effectively handling false alarms and reducing computational complexity.

Contribution

It presents a novel DP-based clustering approach for radio SLAM that estimates the number of landmarks and improves computational efficiency compared to existing methods.

Findings

Effective clustering of reflectors using DP in challenging environments

Slight performance decrease compared to Rao-Blackwellized filter

Significant reduction in computational complexity

Abstract

Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming highly resolvable, enabling accurate vehicle localization and mapping. We propose a novel method of radio simultaneous localization and mapping (SLAM) with the Dirichlet process (DP). The DP, which can estimate the number of clusters as well as clustering, is capable of identifying the locations of reflectors by classifying signals when such 5G signals are reflected and received from various objects. We generate birth points using the measurements from 5G mmWave signals received by the vehicle and classify objects by clustering birth points generated over time. Each time we use the DP clustering method, we can map landmarks in the environment in challenging situations where false alarms exist in the measurements and change the cardinality of received signals. Simulation results demonstrate the performance of the proposed scheme. By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity.