A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation

Zahran, Shady; Moussa, Adel; Sesay, A.B.; El‐Sheimy, Naser

doi:10.1109/jsen.2018.2890094

Cited by 32 publications

(19 citation statements)

References 28 publications

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“…The estimated velocity will be integrated with INS measurements through EKF to estimate better navigation parameters. It is worth noting that the conventional pitot tube used in the fixed wing drones cannot be employed for that type of drones (quadcopters) for these reasons (Zahran et al, 2018b): 1) pitot tube is sensitive to the direction of air flow; 2) it requires high air flow velocity; and 3) small change in velocities of quadcopter did not induce significant differential pressure to be measured by pitot tube.…”

Section: System Overviewmentioning

confidence: 99%

“…Substituting 1 According to the weight of the resisting plate, the dynamic range of velocities that can be measured by Air-Odo can be altered, and the constant value can be calibrated as shown in (Zahran et al, 2018b). In its current state Air-Odo can measure from 0.7 m/s to 5 m/s.…”

Section: Hall-effect Sensor "Higher Velocity Profile Air-odo"mentioning

confidence: 99%

“…In (Barton, 2012), thermopiles were used to find the difference in temperature between the ground and the sky so they can estimate the attitude of the UAV. While in (Zahran et al, 2018b) they introduced unconventional manipulation from the typical use of Hall-effect magnetic sensor. The proposed approach act as a flying odometer for quadcopter (Air-Odo) to estimate its forward velocity.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Uav Navigation Using Hall-Magnetic and Air-Mass Flow Sensors in Indoor Environment

Zahran

Moussa

El‐Sheimy

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The use of Unmanned Aerial Vehicles (UAVs) in many commercial and emergency applications has the potential to dramatically alter several industries, and, in the process, change our attitudes regarding their impact on our daily lives activities. The navigation system of these UAVs mainly depends on the integration between the Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to estimate the positions, velocities, and attitudes (PVT) of the UAVs. However, GNSS signals are not always available everywhere and therefore during GNSS signal outages, the navigation system performance will deteriorate rapidly especially when using low-cost INS. Additional aiding sensors are required, during GNSS signal outages, to bound the INS errors and enhance the navigation system performance. This paper proposes the utilization of two sensors (Hall-magnetic and Air-Mass flow sensors) to act as flying odometer by estimating the UAV forward velocity. The estimated velocity is then integrated with INS through Extended Kalman Filter (EKF) to enhance the navigation solution estimation. A real experiment was carried out with the 3DR quadcopter while the proposed system is attached on the top of the quadcopter. The results showed great enhancement in the navigation system performance with more than 98% improvement when compared to the free running INS solution (dead-reckoning).</p>

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Section: System Overviewmentioning

confidence: 99%

Section: Hall-effect Sensor "Higher Velocity Profile Air-odo"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced Uav Navigation Using Hall-Magnetic and Air-Mass Flow Sensors in Indoor Environment

Zahran

Moussa

El‐Sheimy

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For example, INS has a slowly varying drift, and the navigation error increases over time. 3,4 In visual navigation, the image information to be processed is large, so the real-time performance is poor, and it is easy to be constrained by weather factors such as rain and night. 5 Port AGV working area is generally large, when using lidar SLAM navigation, there are few reference signs, which is not conducive to stitching the map in real time.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Li et al 4 and Du et al, 10 the authors introduce integrated navigation methods based on INS and GNSS. Zahran et al, 3 the author introduces an integrated navigation method based on INS and odometer. However, the positioning accuracy of these methods does not meet the requirements of port heavy-duty AGV loading and unloading containers.…”

Section: Introductionmentioning

confidence: 99%

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

Feng

Duan³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The current navigation methods for port heavy-duty automated guided vehicle mainly include the antenna radar-transponder navigation and the global navigation satellite system. However, the former has a huge cost and the latter will generate multi-path error easily. To avoid these problems, an improved integrated navigation method based on single-axis rotating inertial navigation system, global navigation satellite system and kinematics is proposed. First, the rotating inertial navigation system/ global navigation satellite system and rotating inertial navigation system/Kinematics integrated navigation methods generate corresponding estimates and filtering error covariances through their respective extended Kalman filter filters, and then the two sets of results are fused by the optimal weighted voting fusion method. The proposed method is applied to a heavy-duty automated guided vehicle for engineering verification. Without multi-path error, the navigation accuracy is 1.8–2.98 times higher than that of the traditional global navigation satellite system navigation. In the case of multi-path error, the improved method still has high fault tolerance and high navigation accuracy. The accuracy of this method satisfies the requirements of port heavy-duty automated guided vehicle, which can greatly reduce the number of transponders and has high practical value.

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Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters

Gao

et al. 2021

Appl Intell

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A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation

Cited by 32 publications

References 28 publications

Enhanced Uav Navigation Using Hall-Magnetic and Air-Mass Flow Sensors in Indoor Environment

Enhanced Uav Navigation Using Hall-Magnetic and Air-Mass Flow Sensors in Indoor Environment

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters

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