Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs aided by Ground Robots

Yu, Xianjia; Li, Qingqing; Queralta, Jorge Peña; Heikkonen, Jukka; Westerlund, Tomi

doi:10.1109/icas49788.2021.9551177

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…This applies to various applications, including autonomous docking ( Nguyen et al, 2019 ), collaborative scene reconstruction ( Queralta et al, 2022 ), and others. By integrating UWB with other sensors including visual odometry, and GNSS, the robots can obtain more accurate and robust relative positions ( Qi et al, 2020 ; Xu et al, 2020 ; Xianjia et al, 2021b ).…”

Section: Related Workmentioning

confidence: 99%

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Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization

Salimpour,

Morón,

et al. 2023

Front. Robot. AI

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Ultra-wideband (UWB) localization methods have emerged as a cost-effective and accurate solution for GNSS-denied environments. There is a significant amount of previous research in terms of resilience of UWB ranging, with non-line-of-sight and multipath detection methods. However, little attention has been paid to resilience against disturbances in relative localization systems involving multiple nodes. This paper presents an approach to detecting range anomalies in UWB ranging measurements from the perspective of multi-robot cooperative localization. We introduce an approach to exploiting redundancy for relative localization in multi-robot systems, where the position of each node is calculated using different subsets of available data. This enables us to effectively identify nodes that present ranging anomalies and eliminate their effect within the cooperative localization scheme. We analyze anomalies created by timing errors in the ranging process, e.g., owing to malfunctioning hardware. However, our method is generic and can be extended to other types of ranging anomalies. Our approach results in a more resilient cooperative localization framework with a negligible impact in terms of the computational workload.

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Section: Related Workmentioning

confidence: 99%

“…reconstruction (Queralta et al, 2022), and others. By integrating UWB with other sensors including visual odometry, and GNSS, the robots can obtain more accurate and robust relative positions (Qi et al, 2020;Xu et al, 2020;Xianjia et al, 2021b).…”

Section: Figurementioning

confidence: 99%

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization

Salimpour,

Morón,

et al. 2023

Front. Robot. AI

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“…These applications might not provide a controlled environment for a system based on fixed anchors. Onboard odometry approaches like the use of lidar and VIO, might in time cause drifting and the integration of UWB can aid in correcting it and reducing long-term drift [13], [32].…”

Section: B Uwb In Mobile Robotsmentioning

confidence: 99%

Towards Large-Scale Relative Localization in Multi-Robot Systems with Dynamic UWB Role Allocation

Morón¹,

Queralta²,

Westerlund³

2022

Preprint

Self Cite

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Ultra-wideband (UWB) ranging has emerged as a key radio technology for robot positioning and relative localization in multi-robot systems. Multiple works are now advancing towards more scalable systems, but challenges still remain. This paper proposes a novel approach to relative localization in multirobot systems where the roles of the UWB nodes are dynamically allocated between active nodes (using time-of-flight for ranging estimation to other active nodes) and passive nodes (using timedifference-of-arrival for estimating range differences with respect to pairs of active nodes). We adaptively update UWB roles based on the location of the robots with respect to the convex envelope defined by active nodes, and introducing constraints in the form of localization frequency and accuracy requirements. We demonstrate the applicability of the proposed approach and show that the localization errors remain comparable to fixed-role systems. Then, we show how the navigation of an autonomous drone is affected by the changes in the localization system, obtaining significantly better trajectory tracking accuracy than when relying in passive localization only. Our results pave the way for UWB-based localization in large-scale multi-robot deployments, for either relative positioning or for applications in GNSS-denied environments.

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“…Huang et al [5] used AprilTags to construct a relative spatial coordinate system to provide accurate coordinate distribution for the algorithm, then proposed a time series prediction model [6] to correct unexpected errors. The data can be updated by replacing similar sensors [7] or establishing active robot exploration criteria [8], allowing for better localization accuracy. Fusion methods can be broadly categorized into two major classes: methods based on filtering [9][10][11], and methods based on optimization [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

Zhang,

Liu,

2023

Applied Sciences

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Accurate localization of robots in unstructured environments poses challenges due to low localization accuracy and local trajectory oscillation caused by complex feature points when using Euclidean-based filtering methods. In this study, we propose a novel 3D robot localization method named LIF-M that leverages a manifold-based approach in conjunction with an unscented Kalman filter (UKF-M). Additionally, a relocalization algorithm is designed to ensure localization stability. The proposed method addresses the limitations of conventional Euclidean-based filtering methods by incorporating manifold-based techniques, providing a more comprehensive representation of the complex geometric features. We introduce the manifold concept, where the relevant definition is defined and utilized within the LIF-M framework. By combining left and right invariants, we effectively reduce noise uncertainty, resulting in improved localization accuracy. Moreover, we employ sigma points as a matrix representation of the state points’ space in order to seamlessly transition between the matrix space and the vector representation of the tangent space. Experimental tests and error calculations were conducted to evaluate the performance of various algorithm frameworks, and the results demonstrated the importance of the manifold-based approach for accurate attitude estimation. Compared to the standard UKF, the manifold space equips LIF-M with better robustness and stability in unstructured environments.

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Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs aided by Ground Robots

Cited by 16 publications

References 24 publications

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization

Exploiting redundancy for UWB anomaly detection in infrastructure-free multi-robot relative localization

Towards Large-Scale Relative Localization in Multi-Robot Systems with Dynamic UWB Role Allocation

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

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