scholarly journals An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2952 ◽  
Author(s):  
Xingxing Guang ◽  
Yanbin Gao ◽  
Henry Leung ◽  
Pan Liu ◽  
Guangchun Li
Keyword(s):  
Navigation System ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Visual Navigation ◽  
Measurement Unit ◽  
Random Drift ◽  
Vehicle Navigation System ◽  
Autonomous Vehicle Navigation ◽  
Navigation Accuracy ◽  
Navigation Errors

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.

scholarly journals High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 899 ◽  
Author(s):  
Veli Ilci ◽  
Charles Toth
Keyword(s):  
Autonomous Vehicles ◽  
Moving Objects ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Measurement Unit ◽  
High Definition ◽  
Mobile Mapping ◽  
Autonomous Vehicle Navigation

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

scholarly journals A LabVIEW-based Autonomous Vehicle Navigation System using Robot Vision and Fuzzy Control

2011 ◽  
Vol 12 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Juan Manuel Ramírez-Cortés ◽  
◽  
Pilar Gómez-Gil ◽  
Jorge Martínez-Carballido ◽  
Filiberto López-Larios ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Navigation System ◽  
Autonomous Vehicle ◽  
Robot Vision ◽  
Vehicle Navigation ◽  
Vehicle Navigation System ◽  
Autonomous Vehicle Navigation

Grid-based Environment Estimation for Local Autonomous Vehicle Navigation

2016 ◽  
Author(s):  
Georg Tanzmeister
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Local Environment ◽  
Sensor Data ◽  
Test Vehicle ◽  
Safety Critical ◽  
Environment Model ◽  
Reliable Model ◽  
Autonomous Vehicle Navigation ◽  
Grid Based

This dissertation is focused on the environment model for automated vehicles. A reliable model of the local environment available in real-time is a prerequisite to enable almost any useful ­activity performed by a robot, such as planning motions to fulfill tasks. It is particularly important in safety critical applications, such as for autonomous vehicles in regular traffic. In this thesis, novel concepts for local mapping, tracking, the detection of principal moving directions, cost evaluations in motion planning, and road course estimation have been developed. An object- and sensor-independent grid representation forms the basis of all presented methods enabling a generic and robust estimation of the environment. All approaches have been evaluated with sensor data from real road scenarios, and their performance has been experimentally demonstrated with a test vehicle. ...

GPS-INS Integration Algorithm for Autonomous Vehicle Using Loosely Coupled Integration Architecture

2013 ◽  
Vol 390 ◽  
pp. 506-511
Author(s):  
Rashid Iqbal ◽  
Zhong Jian Li ◽  
Khan Badshah
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Initial Alignment ◽  
Integration Algorithm ◽  
Loosely Coupled ◽  
Integration Architecture

Inertial measurement unit (IMU) has been widely used for autonomous vehicles navigation. The accuracy of IMU specifies the performance of the inertial navigation system (INS).The errors in the INS are mainly due to the IMU inaccuracies, initial alignment, computational errors and approximations in the system equations. These errors are further integrated over time due to the dead-reckoning nature of the INS, which leads to unacceptable results. These errors need an accurate estimation for high precision navigation. INS is integrated with Global Positioning System (GPS) to estimate the errors and enhance the navigation capability of the INS. Linearized Kalman Filter (LKF) is proposed for estimating the errors in the low cost INS using Loosely Coupled integration approach, which is opted for its simplicity and robustness. Prediction part of the LKF is used during the GPS lag for errors estimation, which is found very effective for low cost sensors. The resulting GPS-INS integration algorithm is evaluated on simulated Autonomous vehicle trajectory, generated from 6-DOF model. The integrated system limits the attitude errors less than 0.1 deg and velocity errors of the order of 0.003 meter per second. Furthermore, it provides an optimal navigation solution than can be achieved from individual systems.

A self-calibration method for non-orthogonal angles of gimbals in rotational inertial navigation system based on fiber optic gyro

2017 ◽  
Vol 40 (13) ◽  
pp. 3665-3674 ◽  
Author(s):  
Zengjun Liu ◽  
Lei Wang ◽  
Wei Wang ◽  
Tianxiao Song
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Fiber Optic ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Unit ◽  
Fiber Optic Gyroscope ◽  
Self Calibration ◽  
Photoelectric Encoder ◽  
Navigation Accuracy

Rotating modulation technique is a mature method that has been widely used in the rotational inertial navigation system (RINS). Tri-axis RINS has three gimbals, and the Inertial Measurement Unit can rotate along three directions to modulate the inertial devices’ errors, so that the navigation accuracy of the system can be greatly improved. However, the outputs of attitudes are easily affected by the non-orthogonal angles of gimbals, which should be accurately calibrated and compensated. In this paper, the effects of the non-orthogonal angles on the attitudes are discussed detailed and simulations based on Matlab are conducted to verify that firstly; then, a self-calibration method based on the outputs of the fiber optic gyroscope and photoelectric encoder is proposed. Experimental results in a real tri-axis RINS show that the attitude outputs accuracy are improved from 150” to less than 10”, which verify the practicability of the calibration method proposed in this paper.

scholarly journals Modified Sage-Husa Adaptive Kalman Filter-Based SINS/DVL Integrated Navigation System for AUV

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ruixin Liu ◽  
Fucheng Liu ◽  
Chunning Liu ◽  
Pengchao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Positive Definiteness ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
The Stability ◽  
Navigation Accuracy ◽  
Navigation Errors

This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

scholarly journals Sensors and Sensor’s Fusion in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6586
Author(s):  
Andrzej Stateczny ◽  
Marta Wlodarczyk-Sielicka ◽  
Pawel Burdziakowski
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Navigation ◽  
Autonomous Vehicle Navigation

Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...]

A Versatile Visual Navigation System for Autonomous Vehicles

2019 ◽  
pp. 90-110 ◽  
Author(s):  
Filip Majer ◽  
Lucie Halodová ◽  
Tomáš Vintr ◽  
Martin Dlouhý ◽  
Lukáš Merenda ◽  
...  
Keyword(s):  
Navigation System ◽  
Autonomous Vehicles ◽  
Visual Navigation
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