IMU/Magnetometer/Barometer/Mass-Flow Sensor Integrated Indoor Quadrotor UAV Localization with Robust Velocity Updates

Li, You; Zahran, Shady; Zhuang, Yuan; Гао, Зонгшенг; Luo, Yiran; He, Zhe; Pei, Ling; Chen, Ruizhi; El‐Sheimy, Naser

doi:10.3390/rs11070838

Cited by 31 publications

(28 citation statements)

References 48 publications

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“…Barometers measure the air pressure and are integrated in many wearable and mobile devices and drones currently, since the atmospheric pressure difference can be used to estimate the change of the altitude elevation [ 42 , 43 , 44 ]. In [ 45 , 46 ], integrated barometers were placed inside face masks to monitor breathing activities without distracting the wearer, from which the intensity was classified as “normal breathing” and “deep breathing” through machine learning approaches.…”

Section: Background and State-of-the-artmentioning

confidence: 99%

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Zhou

Costa

Lukowicz

2020

Sensors

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Cardiorespiratory (CR) signals are crucial vital signs for fitness condition tracking, medical diagnosis, and athlete performance evaluation. Monitoring such signals in real-life settings is among the most widespread applications of wearable computing. We investigate how miniaturized barometers can be used to perform accurate spirometry in a wearable system that is built on off-the-shelf training masks often used by athletes as a training aid. We perform an evaluation where differential barometric pressure sensors are compared concurrently with a digital spirometer, during an experimental setting of clinical forced vital capacity (FVC) test procedures with 20 participants. The relationship between the two instruments is derived by mathematical modeling first, then by various regression methods from experiment data. The results show that the error of FVC vital values between the two instruments can be as low as 2∼3%. Beyond clinical tests, the method can also measure continuous tidal breathing air volumes with a 1∼3% error margin. Overall, we conclude that barometers with millimeter footprints embedded in face mask apparel can perform similarly to a digital spirometer to monitor breathing airflow and volume in pulmonary function tests.

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Section: Background and State-of-the-artmentioning

confidence: 99%

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Zhou

Costa

Lukowicz

2020

Sensors

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“…There are indoor PLAN solutions based on other types of environmental signals, such as the magnetic (Kok and Solin 2018), acoustic (Wang et al 2017), air pressure (Li et al 2018), visible light (Zhuang et al 2019), and mass flow (Li et al 2019a).…”

Section: G/lpwanmentioning

confidence: 99%

“…This use case integrated a low-cost IMU, a barometer, a mass-flow sensor, and ultrasonic sensors for indoor drone navigation (Li et al 2019a). The forward velocity from the mass flow sensor and the lateral and vertical NHC can be utilized for 3D velocity updates.…”

Section: Dronesmentioning

confidence: 99%

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Indoor navigation: state of the art and future trends

El‐Sheimy

2021

Satell Navig

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157

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This paper reviews the state of the art and future trends of indoor Positioning, Localization, and Navigation (PLAN). It covers the requirements, the main players, sensors, and techniques for indoor PLAN. Other than the navigation sensors such as Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), the environmental-perception sensors such as High-Definition map (HD map), Light Detection and Ranging (LiDAR), camera, the fifth generation of mobile network communication technology (5G), and Internet-of-Things (IoT) signals are becoming important aiding sensors for PLAN. The PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors, multi-platform/multi-device/multi-sensor information fusion, self-learning systems, and the integration with artificial intelligence, 5G, IoT, and edge/fog computing.

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“…Algorithm 2 gives the details of the algorithm. In this section we integrate the PDR position with the beacon and GNSS/DGNSS observations to obtain a fusion position using EKF [35][36][37]. PDR theory indicates that the current location of a smart phone can be calculated by its previous location combined with moved distance and heading, which depend on the detected number of steps and estimated step length.…”

Section: Gnss/dgnss Positioning and Noise Evaluationmentioning

confidence: 99%

Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

Luo

et al. 2019

Remote Sensing

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This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % .

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IMU/Magnetometer/Barometer/Mass-Flow Sensor Integrated Indoor Quadrotor UAV Localization with Robust Velocity Updates

Cited by 31 publications

References 48 publications

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Indoor navigation: state of the art and future trends

Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

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