scholarly journals Comparison of Three Off-the-Shelf Visual Odometry Systems

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 56 ◽  
Author(s):  
Alexandre Alapetite ◽  
Zhongyu Wang ◽  
John Paulin Hansen ◽  
Marcin Zajączkowski ◽  
Mikołaj Patalan
Keyword(s):  
Ground Truth ◽  
Visual Odometry ◽  
Measurement Unit ◽  
Positioning System ◽  
Fully Integrated ◽  
Global Positioning ◽  
Comparable Performance ◽  
Robot Position ◽  
Visual Clues ◽  
Better Than

Positioning is an essential aspect of robot navigation, and visual odometry an important technique for continuous updating the internal information about robot position, especially indoors without GPS (Global Positioning System). Visual odometry is using one or more cameras to find visual clues and estimate robot movements in 3D relatively. Recent progress has been made, especially with fully integrated systems such as the RealSense T265 from Intel, which is the focus of this article. We compare between each other three visual odometry systems (and one wheel odometry, as a known baseline), on a ground robot. We do so in eight scenarios, varying the speed, the number of visual features, and with or without humans walking in the field of view. We continuously measure the position error in translation and rotation thanks to a ground truth positioning system. Our result shows that all odometry systems are challenged, but in different ways. The RealSense T265 and the ZED Mini have comparable performance, better than our baseline ORB-SLAM2 (mono-lens without inertial measurement unit (IMU)) but not excellent. In conclusion, a single odometry system might still not be sufficient, so using multiple instances and sensor fusion approaches are necessary while waiting for additional research and further improved products.

scholarly journals Real-world Comparison of Visual Odometry Methods

2020 ◽  
Author(s):  
Alexandre Alapetite ◽  
Zhongyu Wang ◽  
John Paulin Hansen ◽  
Marcin Zajączkowski ◽  
Mikolaj Patalan
Keyword(s):  
Ground Truth ◽  
Visual Odometry ◽  
Visual Features ◽  
Fully Integrated ◽  
Result Show ◽  
Comparable Performance ◽  
Internal Information ◽  
Robot Position ◽  
Visual Clues ◽  
Do So

Positioning is an essential aspect of robot navigation, and visual odometry an important technique for continuous updating the internal information about robot position, especially indoors without GPS. Visual odometry is using one or more cameras to find visual clues and estimate robot movements in 3D relatively. Recent progress has been made, especially with fully integrated systems such as the RealSense T265 from Intel, which is the focus of this article. We compare between each other three visual odometry systems and one wheel odometry, on a ground robot. We do so in 8 scenarios, varying the speed, the number of visual features, and with or without humans walking in the field of view. We continuously measure the position error in translation and rotation thanks to a ground truth positioning system. Our result show that all odometry systems are challenged, but in different ways. In average, ORB-SLAM2 has the poorer results, while the RealSense T265 and the Zed Mini have comparable performance. In conclusion, a single odometry system might still not be sufficient, so using multiple instances and sensor fusion approaches are necessary while waiting for additional research and further improved products.

Cartesian Control for the Inertial Measurement Unit Calibration Platform

2000 ◽  
Author(s):  
John J. Hall ◽  
Robert L. Williams ◽  
Frank van Graas
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Vertical Motion ◽  
High Accuracy ◽  
Measurement Unit ◽  
Positioning System ◽  
Ohio University ◽  
Inertial Measurement ◽  
Gps Technology ◽  
Global Positioning

Abstract The Department of Mechanical Engineering and the Avionics Engineering Center at Ohio University are developing an electromechanical system for the calibration of an inertial measurement unit (IMU) using global positioning system (GPS) antennas. The GPS antennas and IMU are mounted to a common platform to be oriented in the angular roll, pitch, and yaw motions. Vertical motion is also included to test the systems in a vibrational manner. A four-dof system based on the parallel Carpal Wrist is under development for this task. High-accuracy positioning is not required from the platform since the GPS technology provides absolute positioning for the IMU calibration process.

The Global Positioning System

1980 ◽  
Vol 294 (1410) ◽  
pp. 395-406 ◽  
Keyword(s):  
United States ◽  
Travel Time ◽  
Global Positioning System ◽  
The United States ◽  
Positioning System ◽  
Absolute Position ◽  
Southwestern Part ◽  
Ground Station ◽  
Global Positioning ◽  
Better Than

The Navstar Global Positioning System, proposed for deployment in 1985, will have eight satellites equally spaced in each of three orbit planes at an inclination of 63°. Since the satellites will be in circular, 20000 km (12 h period), orbits and the nodes of the three orbit planes will be equally spaced, at least four satellites will be in view at any location. Range to three of the satellites, computed from the travel time of signal from the satellite to the ground, would give the position of the ground receiver. The measurement to the fourth satellite is required to synchronize the ground station clock with the satellite to provide a sufficiently accurate travel time. In order that the system may be demonstrated in early 1979, six satellites are now being launched into orbits that will provide the operational configuration over the southwestern part of the United States for a few hours each day. The accuracy of the instantaneous absolute position is expected to be 10 m. The relative position of two stationary receivers could be determined to 1 m accuracy in a few minutes even if the receivers are separated by 1000 km. Relative positions could be determined to better than 10 cm accuracy within a day.

scholarly journals Spoofing Detection of Civilian UAVs Using Visual Odometry

2019 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Masood Varshosaz ◽  
Alireza Afary ◽  
Barat Mojaradi ◽  
Mohammad Saadatseresht ◽  
Ebadat Ghanbari Parmehr
Keyword(s):  
Global Positioning System ◽  
Detection Method ◽  
Visual Odometry ◽  
Positioning System ◽  
Moving Window ◽  
Gps Receiver ◽  
Flight Direction ◽  
Global Positioning ◽  
Spoofing Detection ◽  
Euclidian Distances

Spoofing of Unmanned Aerial Vehicles (UAV) is generally carried out through spoofing of the UAV’s Global Positioning System (GPS) receiver. This paper presents a vision-based UAV spoofing detection method that utilizes Visual Odometry (VO). This method is independent of the other complementary sensors and any knowledge or archived map and datasets. The proposed method is based on the comparison of relative sub-trajectory of the UAV from VO, with its absolute replica from GPS within a moving window along the flight path. The comparison is done using three dissimilarity measures including (1) Sum of Euclidian Distances between Corresponding Points (SEDCP), (2) angle distance and (3) taxicab distance between the Histogram of Oriented Displacements (HOD) of these sub-trajectories. This method can determine the time and location of UAV spoofing and bounds the drift error of VO. It can be used without any restriction in the usage environment and can be implemented in real-time applications. This method is evaluated on four UAV spoofing scenarios. The results indicate that this method is effective in the detection of UAV spoofing due to the Sophisticated Receiver-Based (SRB) GPS spoofing. This method can detect UAV spoofing in the long-range UAV flights when the changes in UAV flight direction is larger than 3° and in the incremental UAV spoofing with the redirection rate of 1°. Additionally, using SEDCP, the spoofing of the UAV, when there is no redirection and only the velocity of the UAV is changed, can be detected. The results show that SEDCP is more effective in the detection of UAV spoofing and fake GPS positions.

An autonomous rice transplanter guided by global positioning system and inertial measurement unit

2009 ◽  
Vol 26 (6-7) ◽  
pp. 537-548 ◽  
Author(s):  
Yoshisada Nagasaka ◽  
Hidefumi Saito ◽  
Katsuhiko Tamaki ◽  
Masahiro Seki ◽  
Kyo Kobayashi ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Positioning System ◽  
Inertial Measurement ◽  
Global Positioning

scholarly journals Vehicle location measurement method for radio-shadow area through iBeacon message

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881257
Author(s):  
ChoonSung Nam ◽  
Dong-Ryeol Shin
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Positioning System ◽  
Inertial Measurement ◽  
Location Data ◽  
Shadow Area ◽  
Global Positioning ◽  
Vehicle Location ◽  
Roadside Unit

Information communication technology related vehicle services need to support location and the transmission of communication and traffic information between vehicles, or between vehicles and infrastructure. In particular, the technology for the measurement of the accurate location of a vehicle is dependent on location-determination technology like Global Positioning System, and this technology is very important for vehicle driving and location services. If, however, a vehicle is in a Global Positioning System radio-shadow area, neither a Global Positioning System nor a Differential Global Positioning System can accurately measure the corresponding location because of a high error rate caused by the shadowing intervention. Even an Inertial Measurement Unit could provide inaccurate location data due to sensor drift faults around corners and traffic-road speed dumps. Vehicles, therefore, need an absolute location to prevent the provision of inaccurate vehicle-location data that is due to radio-shadow areas and relational Inertial Measurement Unit positions. To achieve this, we assume that vehicle-to-infrastructure communication is possible between a vehicle and roadside unit in Vehicular Ad hoc Networks. We used iBeacon at the roadside unit and revised its Universally Unique Identifier so that it generates absolute Global Positioning System location data; that is, moving vehicles can receive absolute Global Positioning System data from the roadside unit-based iBeacon. We compared the proposed method with current Global Positioning System and Inertial Measurement Unit systems for the following two cases: one with a radio-shadow area and one without. We proved that the proposed method generates location data that are more accurate than those of the other methods.

scholarly journals Sistem Monitoring Konversi Wifi-RSSI Menjadi Jarak Antar Device dan Router Menggunakan Node-Red Pada Pengembangan Sistem Navigasi Dalam Ruang

2021 ◽  
Vol 1 ◽  
pp. 32-37
Author(s):  
Rifqi Nafis Mubaroq ◽  
Nina Siti Aminah
Keyword(s):  
Global Positioning System ◽  
Raspberry Pi ◽  
Positioning System ◽  
Actual Distance ◽  
Comparison Results ◽  
Graph Match ◽  
Global Positioning ◽  
Narrow Space ◽  
Narrow Area ◽  
Better Than

A global positioning system (GPS) is often used as a tool for determining direction and positioning. However, GPS is weak in estimating the error, which is quite large at the actual distance, so positioning in a narrow area cannot work well. Therefore, a better method is needed to be used as a narrow-navigation system. This study aimed to determine whether RSSI has a better error than GPS for navigation devices in a narrow space or area. The method used was plotting graphs using Ms. Excel and determining the value of R square. The RSSI value was obtained through the Esp8266 device and then sent to the server via the MQTT protocol and converted using Node-Red on the Raspberry Pi. The comparison results of the Esp8266 device RSSI graph match the Laptop RSSI device as a reference. The match between the relative distance to the RSSI with the actual distance shows a 3.3-33% error. This result is considered much better than the GPS error.

scholarly journals Uji Presisi dari Nonholonomic Mobile Robot pada Rancang Bangun Sistem Navigasi

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Desmas A. Patriawan ◽  
Bagoes P. Natakusuma ◽  
Ahmad Anas Arifin ◽  
Hasan S. Maulana ◽  
Hery Irawan ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Global Positioning System ◽  
Measurement Unit ◽  
Positioning System ◽  
Nonholonomic Mobile Robot ◽  
Global Positioning

Navigasi menjadi bagian yang penting bagi kendaraan. Global positioning system (GPS) merupakan sistem navigasi yang paling banyak digunakan pada kendaraan. Namun dengan akurasi 5-10 meter membuat GPS tidak bisa diaplikasikan dalam bagian sistem kendali pada kendaraan. Penambahan sensor inertia measurement unit (IMU) diharapkan mampu menambahkan akurasi pada Gerakan kendaraan. Kendaran yang digunakan adalah robot beroda dengan sistem nonholonomic. Pada robot ini dipasang Sensor IMU, GPS dan kontroler supaya robot tersebut bisa berputar lalu melaju secara lurus dengan kordinat yang sudah ditentukan. Hasil pengujian didapatkan robot memiliki respon time sebesar 4.1 detik tanpa kontroler dan 2.1 detik dengan kontroler. Akurasi sudut dari 5  menjadi 2 .

scholarly journals Win, Draw, or Lose? Global Positioning System-Based Variables’ Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5695
Author(s):  
Hadi Nobari ◽  
Norbert Keshish Banoocy ◽  
Rafael Oliveira ◽  
Jorge Pérez-Gómez
Keyword(s):  
Global Positioning System ◽  
Descriptive Analysis ◽  
Total Duration ◽  
Measurement Unit ◽  
Positioning System ◽  
Warm Up ◽  
Professional Soccer ◽  
Pacing Strategies ◽  
Global Positioning ◽  
Soccer Team

The aim of the study was to determine the between-match and between-halves match variability of various Global Positioning System (GPS) variables and metabolic power average (MPA) in competitions, based on the match results obtained by professional soccer players over a full season. Observations on individual match performance measures were undertaken on thirteen outfield players competing in the Iranian Premier League. The measures selected for analysis included total duration, accelerations in zones (AccZ1, 2, and 3), decelerations in zones (DecZ1, 2, and 3), and MPA collected by the Wearable Inertial Measurement Unit (WIMU). The GPS manufacturer set the thresholds for the variables analyzed as follows: AccZ1 (<2 m.s−2); AccZ2 (2 to 4 m.s−2); AccZ3 (>4 m.s−2); DecZ1 (<−2 m.s−2); DecZ2 (−2 to −4 m.s−2); DecZ3 (>−4 m.s−2). The results revealed significant differences between wins and draws for the duration of the match and draws compared to wins for the first- half duration (p ≤ 0.05; ES = 0.36 [−0.43,1.12]), (p ≤ 0.05; ES = −7.0 [−8.78, −4.78], respectively. There were significant differences on AccZ1 during the first-half between draws and defeats (p ≤ 0.05; ES = −0.43 [−1.32,0.46]), for AccZ3 in the second-half between draws and defeats (p ≤ 0.05; ES = 1.37 [0.48,2.25]). In addition, there were significant differences between wins and draws (p ≤ 0.05; ES = 0.22 [−0.62,1.10]), and wins and defeats for MPA in the first- half (p ≤ 0.05; ES = 0.34 [−0.65,1.22]). MPA showed further differences between draws and defeats in the second- half (p ≤ 0.05; ES = 0.57 [−0.22,1.35]). Descriptive analysis revealed differences between the first and second half for wins in AccZ2 (p = 0.005), DecZ2 (p = 0.029), and MPA (p = 0.048). In addition, draws showed significant differences between the first and second half in duration, AccZ1, AccZ2, and DecZ2 (p = 0.008), (p = 0.017), (p = 0.040), and (p = 0.037) respectively. Defeats showed differences between the first and second half in AccZ1, AccZ3, and MPA (p = 0.001), (p = 0.018), and (p = 0.003) respectively. In summary, the study reveals large variations between the match duration, accelerometer variables, and MPA both within and between matches. Regardless of the match outcome, the first half seems to produce greater outputs. The results should be considered when performing a half-time re-warm-up, as this may be an additional factor influencing the drop in the intensity markers in the second half in conjunction with factors such as fatigue, pacing strategies, and other contextual variables that may influence the results.

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