The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter

Wang, Yan; Li, Xin

doi:10.3390/ijgi6080235

Cited by 44 publications

(26 citation statements)

References 19 publications

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“…Because a single positioning scheme is unable to solve all the problems of indoor positioning completely, indoor integrated navigation positioning technologies have become one of the key research areas. Typical indoor positioning schemes include UWB and SINS (strap-down inertial navigation system) indoor integrated navigation [7,8,9], and UWB and visual simultaneous localization and mapping (SLAM) integrated navigation [10], and so on. In order to improve the robustness of the positioning system, some scholars proposed UWB, INS (inertial navigation system) and visual SLAM integrated navigation [11].…”

Section: Introductionmentioning

confidence: 99%

An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

Liu

Zhao

et al. 2019

Sensors

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In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m.

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Section: Introductionmentioning

confidence: 99%

An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

Liu

Zhao

et al. 2019

Sensors

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“…However, the devices are bulky with wired connections, which is inconvenient for wearable applications. Wang et al [48] integrated IMU and UWB devices and use particle filter algorithms for data fusion to achieve pedestrian positioning. Experimental results show that the algorithm can improve the positioning error to about 0.7 m, which is not ideal for many indoor localization applications.…”

Section: (5) Combined Indoor Localization Solutionsmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

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“…Conventional algorithmic sensor-fusion approaches (e.g. Kalman [21,57,59] and Particle filters [61]) that offer this first layer of diversity are robust to operate in various environments in the wild, but are unable to effectively sift out the erroneous artifacts of individual sensors and fail to provide effective fusion that can deliver high accuracies sustainably. On the other hand, recent data-driven approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Fusing VO's relative tracking with RF's absolute location estimates can enable accurate tracking in a global reference point (RF anchor) as well as relative to one-another.3) Limitations of today's sensor fusion methodologies: Current sensor fusion methods can be broadly divided into two classes of solutions: 1) algorithmic or 2) data-driven models. Algorithmic solutions (e.g., Kalman Filter (KF)[57] and Bayesian Particle Filters[61]) aim to minimize statistical noise using time series data from individual sensors. However, we observe that these approaches consistently under-perform when fusing UWB and ORB-SLAM3.…”

mentioning

confidence: 99%

RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

Dasari¹,

Sheshadri²,

Sundaresan³

et al. 2022

Preprint

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The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments -a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously.In this work, we take an important step towards this goal by introducing the notion of dual-layer diversity to the problem of sensor fusion in multi-agent tracking. We demonstrate that the fusion of complementary tracking modalities, -passive/relative (e.g. visual odometry) and active/absolute tracking (e.g.infrastructure-assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data-driven (for accuracy) approaches. RoVaR is an embodiment of such a dual-layer diversity approach that intelligently attends to cross-modal information using algorithmic and data-driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal RoVaR's multi-dimensional benefits in terms of tracking accuracy (median of ≈15cm), robustness (in unseen environments), light weight (runs in real-time on mobile platforms such as Jetson Nano/TX2), to enable practical multi-agent immersive applications in everyday environments.

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The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter

Cited by 44 publications

References 19 publications

An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

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