Wind Gust Measuring at Low Altitude Using An Unmanned Aerial System

Aerial Platform

A small unmanned aerial vehicle (UAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes within the atmospheric boundary layer (ABL). These gusts have major impacts on the flight characteristics and performance of modern small unmanned aerial vehicles. Hence, this project was set to investigate the power spectral density of gusts observed at low altitudes by measuring the gusts with an aerial platform. The small UAV carried an air-data system including a fivehole probe that was adapted for this specific application. The air-data system measured the local wind gusts with an accuracy of 0.5 m/s by combining inputs from a five-hole probe, an inertial measurement unit, and Global Navigation Satellite System (GNSS) receivers. Over 20 flights were performed during the development of the aerial platform. Airborne experiments were performed to collect gust data at low altitudes between 50 m and 100 m. The result was processed into turbulence spectrum and the measurements were compared with the MIL-HDBK-1797 von K´arm´an turbulence model and the results have shown the model underpredicted the gust intensities experienced by the flight vehicle. The anisotropic properties of low-altitude turbulence were also observed when analyzing the measured gusts spectra. The wind and gust data collected are useful for verifying the existing turbulence models for low-altitude flights and benefit the future development of small UAVs in windy environment.

Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

Journal of Navigation ◽

10.1017/s037346332100031x ◽

2021 ◽

pp. 1-13

Author(s):

Jonghyuk Kim ◽

Jose Guivant ◽

Martin L. Sollie ◽

Torleiv H. Bryne ◽

Tor Arne Johansen

Keyword(s):

Unmanned Aerial Vehicle ◽

Computational Cost ◽

Satellite System ◽

Measurement Unit ◽

Electronic Systems ◽

Computationally Efficient ◽

Global Correlation ◽

Aerial Vehicle ◽

Correlation Information ◽

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

OUTDOOR AR-APPLICATION FOR THE DIGITAL MAP TABLE

10.5194/isprs-archives-xliv-3-w1-2020-93-2020 ◽

2020 ◽

Vol XLIV-3/W1-2020 ◽

pp. 93-98

Author(s):

S. Maier ◽

T. Gostner ◽

F. van de Camp ◽

A. H. Hoppe

Keyword(s):

Augmented Reality ◽

Geographical Location ◽

Satellite System ◽

Reference Points ◽

Sensor Data ◽

Measurement Unit ◽

Positional Accuracy ◽

Digital Map ◽

The World ◽

Abstract. In many fields today, it is necessary that a team has to do operational planning for a precise geographical location. Examples for this are staff work, the preparation of surveillance tasks at major events or state visits and sensor deployment planning for military and civil reconnaissance. For these purposes, Fraunhofer IOSB is developing the Digital Map Table (DigLT). When making important decisions, it is often helpful or even necessary to assess a situation on site. An augmented reality (AR) solution could be useful for this assessment. For the visualization of markers at specific geographical coordinates in augmented reality, a smartphone has to be aware of its position relative to the world. It is using the sensor data of the camera and inertial measurement unit (IMU) for AR while determining its absolute location and direction with the Global Navigation Satellite System (GNSS) and its magnetic compass. To validate the positional accuracy of AR markers, we investigated the current state of the art and existing solutions. A prototype application has been developed and connected to the DigLT. With this application, it is possible to place markers at geographical coordinates that will show up at the correct location in augmented reality at anyplace in the world. Additionally, a function was implemented that lets the user select a point from the environment in augmented reality, whose geographical coordinates are sent to the DigLT. The accuracy and practicality of the placement of markers were examined using geodetic reference points. As a result, we can conclude that it is possible to mark larger objects like a car or a house, but the accuracy mainly depends on the internal compass, which causes a rotational error that increases with the distance to the target.

Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽

10.3390/aerospace8100280 ◽

2021 ◽

Vol 8 (10) ◽

pp. 280

Author(s):

Farzan Farhangian ◽

Hamza Benzerrouk ◽

Rene Landry

Keyword(s):

Kalman Filter ◽

State Space Model ◽

Flight Test ◽

Satellite System ◽

Low Earth Orbit ◽

Measurement Unit ◽

Starting Point ◽

Leo Satellites ◽

Positioning Method ◽

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

Vision Based Indoor Surveillance Patrol Robot Using Extended Dijkstra Algorithm in Path Plannin

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2021.3.2.4 ◽

2021 ◽

Vol 3 (2) ◽

pp. 21-28

Author(s):

Kiat Teh Choon ◽

Kit Wong Wai ◽

Soe Min Thu

Keyword(s):

Path Planning ◽

Satellite System ◽

Measurement Unit ◽

Dijkstra Algorithm ◽

Current Path ◽

Global Positioning ◽

Multiple Cycles ◽

To Come ◽

Robot Path

Vision based patrol robot has been with great interest nowadays due to its consistency, cost effectiveness and no temperament issue. In recent times, Global positioning system (GPS) has been cooperated with Global Navigation Satellite System (GNSS) to come out with better accuracy quality in positioning, navigation, and timing (PNT) services to locate a device. However, such localization service is yet to reach any indoor facility. For an indoor surveillance vision based patrol robot, such limitation hinders its path planning capabilities that allows the patrol robot to seek for the optimum path to reach the appointed destination and return back to its home position. In this paper, a vision based indoor surveillance patrol robot using sensory manipulation technique is presented and an extended Dijkstra algorithm is proposed for the patrol robot path planning. The design of the patrol robot adopted visual type sensor, range sensors and Inertia Measurement Unit (IMU) system to impulsively update the map’s data in line with the patrol robot’s current path and utilize the path planning features to carry out obstacle avoidance and re-routing process in accordance to the obstacle’s type met by the patrol robot. The result conveyed by such approach certainly managed to complete multiple cycles of testing with positive result.

Fused inertial measurement unit and real time kinematic-global navigation satellite system data assessment based on robotic total station information for in-field dynamic positioning

Precision agriculture '15 ◽

10.3920/978-90-8686-814-8_33 ◽

2015 ◽

pp. 275-282 ◽

Cited By ~ 2

Author(s):

D.S. Paraforos ◽

H.W. Griepentrog ◽

J. Geipel ◽

T. Stehle

Keyword(s):

Satellite System ◽

Measurement Unit ◽

Dynamic Positioning ◽

Navigation Satellite ◽

Inertial Measurement ◽

Data Assessment ◽

Field Dynamic ◽

System Data ◽

Robotic Total Station

Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment

Remote Sensing ◽

10.3390/rs11040442 ◽

2019 ◽

Vol 11 (4) ◽

pp. 442 ◽

Cited By ~ 10

Author(s):

Zhen Li ◽

Junxiang Tan ◽

Hua Liu

Keyword(s):

Inertial Measurement Unit ◽

Point Clouds ◽

Calibration Method ◽

Satellite System ◽

Measurement Unit ◽

Inertial Measurement ◽

Self Calibration ◽

Mean Square Errors ◽

Scanning Systems

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration.

Variability and Performance of Short to Long-Range Single Baseline RTK GNSS Positioning in Indonesia

E3S Web of Conferences ◽

10.1051/e3sconf/20199401012 ◽

2019 ◽

Vol 94 ◽

pp. 01012 ◽

Cited By ~ 1

Author(s):

Irwan Gumilar ◽

Brian Bramanto ◽

Fuad F. Rahman ◽

I Made D. A. Hermawan

Keyword(s):

Long Range ◽

High Frequency ◽

Carrier Phase ◽

Satellite System ◽

High Accuracy ◽

Gnss Positioning ◽

And Performance ◽

Gnss Network ◽

Single Baseline ◽

As the modernized Global Navigation Satellite System (GNSS) method, Real Time Kinematic (RTK) ensures high accuracy of position (within several centimeters). This method uses Ultra High Frequency (UHF) radio to transmit the correction data, however, due to gain and power issues, Networked Transport of RTCM via Internet Protocol (RTCM) is used to transmit the correction data for a longer baseline. This Research aims to investigate the performance of short to long-range single baseline RTK GNSS (Up to 80 KM) by applying modified LAMBDA method to resolve the ambiguity in carrier phase. The RTK solution then compared with the differential GNSS network solution. The results indicate that the differences are within RTK accuracy up to 80 km are several centimeter for horizontal solution and three times higher for vertical solution.

Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV

Journal of Unmanned Vehicle Systems ◽

10.1139/juvs-2017-0029 ◽

2018 ◽

Vol 6 (4) ◽

pp. 235-248 ◽

Cited By ~ 2

Author(s):

Alton Yeung ◽

Goetz Bramesfeld ◽

Joon Chung ◽

Stephen Foster

Keyword(s):

Unmanned Aerial Vehicle ◽

Flight Test ◽

Test System ◽

Wind Gusts ◽

Von Karman ◽

Specific Objective ◽

Aerial Vehicle ◽

Low Altitude ◽

And Performance ◽

Von Kármán

A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 500 m. These gusts have significant impact on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models.

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Remote Sensing ◽

10.3390/rs12030411 ◽

2020 ◽

Vol 12 (3) ◽

pp. 411 ◽

Cited By ~ 2

Author(s):

Sangeetha Shankar ◽

Michael Roth ◽

Lucas Andreas Schubert ◽

Judith Anne Verstegen

Keyword(s):

Laser Scanner ◽

Satellite System ◽

Sensor Data ◽

Measurement Unit ◽

Center Line ◽

Railway Tracks ◽

Combination Of Methods ◽

Inertial Measurements ◽

Gnss Data

Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.