scholarly journals Performance Evaluation of IMU and DVL Integration in Marine Navigation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1056 ◽  
Author(s):  
Gen Fukuda ◽  
Daisuke Hatta ◽  
Xiaoliang Guo ◽  
Nobuaki Kubo
Keyword(s):  
Electronic Devices ◽  
Dead Reckoning ◽  
Satellite System ◽  
Measurement Unit ◽  
Tokyo Bay ◽  
Doppler Velocity ◽  
Maritime Logistics ◽  
Global Navigation Satellite ◽  
Micro Electromechanical System ◽  
Marine Navigation

Global navigation satellite system (GNSS) spoofing poses a significant threat to maritime logistics. Many maritime electronic devices rely on GNSS time, positioning, and speed for safe vessel operation. In this study, inertial measurement unit (IMU) and Doppler velocity log (DVL) devices, which are important in the event of GNSS spoofing or outage, are considered in conventional navigation. A velocity integration method using IMU and DVL in terms of dead-reckoning is investigated in this study. GNSS has been widely used for ship navigation, but IMU, DVL, or combined IMU and DVL navigation have received little attention. Military-grade sensors are very expensive and generally cannot be utilized in smaller vessels. Therefore, this study focuses on the use of consumer-grade sensors. First, the performance of a micro electromechanical system (MEMS)-based yaw rate angle with DVL was evaluated using 60 min of raw data for a 50 m-long ship located in Tokyo Bay. Second, the performance of an IMU-MEMS using three gyroscopes and three accelerometers with DVL was evaluated using the same dataset. A gyrocompass, which is equipped on the ship, is used as a heading reference. The results proved that both methods could achieve less than 1 km horizontal error in 60 min.

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

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 ◽  
Global Navigation Satellite

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.

scholarly journals Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 397
Author(s):  
Hossein Shoushtari ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Mobile Networks ◽  
Dead Reckoning ◽  
Simulated Data ◽  
Minimum Cost ◽  
Cost Effective ◽  
Satellite System ◽  
Position Estimation ◽  
Robust Solution ◽  
Position Data ◽  
Global Navigation Satellite

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

scholarly journals Wind Gust Measuring at Low Altitude Using An Unmanned Aerial System

2021 ◽  
Author(s):  
Alton Yeung
Keyword(s):  
Turbulence Models ◽  
Satellite System ◽  
Data System ◽  
Measurement Unit ◽  
Wind Gust ◽  
Wind Gusts ◽  
Low Altitude ◽  
And Performance ◽  
Global Navigation Satellite ◽  
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.

scholarly journals OUTDOOR AR-APPLICATION FOR THE DIGITAL MAP TABLE

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 ◽  
Global Navigation Satellite

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.

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

Aerospace ◽  
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 ◽  
Global Navigation Satellite

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

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 ◽  
Global Navigation Satellite ◽  
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.

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

2019 ◽  
Vol 11 (4) ◽  
pp. 442 ◽  
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 ◽  
Global Navigation Satellite ◽  
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.

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

2020 ◽  
Vol 12 (3) ◽  
pp. 411 ◽  
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 ◽  
Global Navigation Satellite ◽  
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.

Autonomous Navigation of a Mobile Robot Based on GNSS/DR Integration in Outdoor Environments

2014 ◽  
Vol 26 (2) ◽  
pp. 214-224 ◽  
Author(s):  
Taro Suzuki ◽  
◽  
Mitsunori Kitamura ◽  
Yoshiharu Amano ◽  
Nobuaki Kubo ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Dead Reckoning ◽  
Satellite System ◽  
Mobile Robot Navigation ◽  
Observation Data ◽  
Multipath Mitigation ◽  
Ir Camera ◽  
Outdoor Environments ◽  
Global Navigation Satellite

This paper describes the development of a mobile robot system and an outdoor navigationmethod based on global navigation satellite system (GNSS) in an autonomous mobile robot navigation challenge, called the Tsukuba Challenge, held in Tsukuba, Japan, in 2011 and 2012. The Tsukuba Challenge promotes practical technologies for autonomous mobile robots working in ordinary pedestrian environments. Many teams taking part in the Tsukuba Challenge used laser scanners to determine robot positions. GNSS was not used in localization because its positioning has multipath errors and problems in availability. We propose a technique for realizing multipath mitigation that uses an omnidirectional IR camera to exclude “invisible” satellites, i.e., those entirely obstructed by a building and whose direct waves therefore are not received. We applied GPS / dead reckoning (DR) integrated based on observation data from visible satellites determined by the IR camera. Positioning was evaluated during Tsukuba Challenge 2011 and 2012. Our robot ran the 1.4 km course autonomously and evaluation results confirmed the effectiveness of our proposed technique and the feasibility of its highly accurate positioning.

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