Grid-based Hybrid 3DMA GNSS and Terrestrial Positioning

TL;DR

This paper introduces a non-parametric 3DMA grid filter for integrating GNSS, UWB, and vehicle data to improve localization accuracy in challenging urban environments, demonstrating sub-meter precision.

Contribution

It presents a novel 3DMA multi-epoch Grid Filter for tight sensor fusion, addressing synchronization and multi-modality challenges in hybrid GNSS-terrestrial positioning.

Findings

Achieved 0.64 m average error in static tests

Achieved 1.62 m average error in dynamic tests

Proved feasibility of terrestrial signals in 3DMA positioning

Abstract

The paper discusses the increasing use of hybridized sensor information for GNSS-based localization and navigation, including the use of 3D map-aided GNSS positioning and terrestrial systems based on different geometric measurement principles. However, both GNSS and terrestrial systems are subject to negative impacts from the propagation environment, which can violate the assumptions of conventionally applied parametric state estimators. Furthermore, dynamic parametric state estimation does not account for multi-modalities within the state space leading to an information loss within the prediction step. In addition, the synchronization of non-deterministic multi-rate measurement systems needs to be accounted. In order to address these challenges, the paper proposes the use of a non-parametric filtering method, specifically a 3DMA multi-epoch Grid Filter, for the tight integration of GNSS and terrestrial signals. Specifically, the fusion of GNSS, Ultra-wide Band (UWB) and vehicle motion data is introduced based on a discrete state representation. Algorithmic challenges, including the use of different measurement models and time synchronization, are addressed. In order to evaluate the proposed method, real-world tests were conducted on an urban automotive testbed in both static and dynamic scenarios. We empirically show that we achieve sub-meter accuracy in the static scenario by averaging a positioning error of $0.64$ m, whereas in the dynamic scenario the average positioning error amounts to $1.62$ m. The paper provides a proof-of-concept of the introduced method and shows the feasibility of the inclusion of terrestrial signals in a 3DMA positioning framework in order to further enhance localization in GNSS-degraded environments.