Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Giseo Park ◽  
Yoonjin Hwang ◽  
Seibum B. Choi
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Estimation Error ◽  
Nonlinear Observer ◽  
Positioning System ◽  
Positioning Systems ◽  
Noise Covariance ◽  
Heading Angle ◽  
Vehicle Positioning ◽  
Vehicle Sensors

The vehicle positioning system can be utilized for various automotive applications. Primarily focusing on practicality, this paper presents a new method for vehicle positioning systems using low-cost sensor fusion, which combines global positioning system (GPS) data and data from easily available in-vehicle sensors. As part of the vehicle positioning, a novel nonlinear observer for vehicle velocity and heading angle estimation is designed, and the convergence of estimation error is also investigated using Lyapunov stability analysis. Based on this estimation information, a new adaptive Kalman filter with rule-based logic provides robust and highly accurate estimations of the vehicle position. It adjusts the noise covariance matrices Q and R in order to adapt to various environments, such as different driving maneuvers and ever-changing GPS conditions. The performance of the entire system is verified through experimental results using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed.

scholarly journals Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5430 ◽  
Author(s):  
Haigen Min ◽  
Xia Wu ◽  
Chaoyi Cheng ◽  
Xiangmo Zhao
Keyword(s):  
Coordinate System ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Positioning System ◽  
Visual Slam ◽  
Multiple Model ◽  
Positioning Systems ◽  
Local Accuracy ◽  
Global Positioning ◽  
Vehicle Sensors

Real-time, precise and low-cost vehicular positioning systems associated with global continuous coordinates are needed for path planning and motion control in autonomous vehicles. However, existing positioning systems do not perform well in urban canyons, tunnels and indoor parking lots. To address this issue, this paper proposes a multi-sensor positioning system that combines a global positioning system (GPS), a camera and in-vehicle sensors assisted by kinematic and dynamic vehicle models. First, the system eliminates image blurring and removes false feature correspondences to ensure the local accuracy and stability of the visual simultaneous localisation and mapping (SLAM) algorithm. Next, the global GPS coordinates are transferred to a local coordinate system that is consistent with the visual SLAM process, and the GPS and visual SLAM tracks are calibrated with the improved weighted iterative closest point and least absolute deviation methods. Finally, an inverse coordinate system conversion is conducted to obtain the position in the global coordinate system. To improve the positioning accuracy, information from the in-vehicle sensors is fused with the interacting multiple-model extended Kalman filter based on kinematic and dynamic vehicle models. The developed algorithm was verified via intensive simulations and evaluated through experiments using KITTI benchmarks (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and data captured using our autonomous vehicle platform. The results show that the proposed positioning system improves the accuracy and reliability of positioning in environments in which the Global Navigation Satellite System is not available. The developed system is suitable for the positioning and navigation of autonomous vehicles.

GPS/Low-Cost IMU/Onboard Vehicle Sensors Integrated Land Vehicle Positioning System

2007 ◽  
Vol 2007 (1) ◽  
pp. 062616 ◽  
Author(s):  
Jianchen Gao ◽  
MarkG Petovello ◽  
MElizabeth Cannon
Keyword(s):  
Low Cost ◽  
Positioning System ◽  
Land Vehicle ◽  
Vehicle Positioning ◽  
Vehicle Sensors

GPS/Low-Cost IMU/Onboard Vehicle Sensors Integrated Land Vehicle Positioning System

2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Jianchen Gao ◽  
Mark G. Petovello ◽  
M. Elizabeth Cannon
Keyword(s):  
Low Cost ◽  
Positioning System ◽  
Land Vehicle ◽  
Vehicle Positioning ◽  
Vehicle Sensors

A LOW COST MACRO-MICRO POSITIONING SYSTEM WITH SMA-ACTUATED MICRO STAGE

2007 ◽  
Vol 31 (1) ◽  
pp. 75-95 ◽  
Author(s):  
Eric Ho ◽  
Rob Gorbet
Keyword(s):  
Smart Materials ◽  
Lower Cost ◽  
Position Control ◽  
Low Cost ◽  
Experimental System ◽  
Positioning System ◽  
Positioning Systems ◽  
Mechanical Amplification ◽  
Micro Systems ◽  
Better Than

Macro-micro systems allow high-resolution positioning over greater ranges of operation that would be achievable with precision positioning systems. Piezoceramic actuators have established themselves as the principle technology for commercial micro-positioning applications, and the trend in research is to push the limits of resolution down to the nanometer and sub-nanometer scales. Other smart materials offer the potential for lightweight, continuous actuation over small ranges, and hence may be useful in micro-positioning applications. This work focuses on the potential for SMA actuators to enable low-cost micro-positioning. Compared to piezos, SMA offer longer range and lower actuation voltages, enabling lower-cost drive electronics and removing the need for costly precision mechanical amplification stages. A prototype single-axis macro-micro positioning system is described, with a macro range of 200 mm and relative positioning precision of better than 5 5μm. The micro stage is driven by an NM70 SMA actuator from NanoMuscle. Macro and micro stages are modelled and controllers developed, and experimental system performance is evaluated. The success of the system provides an inexpensive platform for the study of macro-micro positioning issues such as stage coupling, friction, and drive flexibility, as well as for the position control of SMA.

scholarly journals A COMPARISON OF UWB AND MOTION CAPTURE UAV INDOOR POSITIONING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1695-1699 ◽  
Author(s):  
A. Masiero ◽  
F. Fissore ◽  
R. Antonello ◽  
A. Cenedese ◽  
A. Vettore
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Motion Capture ◽  
Low Cost ◽  
Ultra Wideband ◽  
Reference Trajectory ◽  
Positioning System ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
3D Positioning ◽  
Adaptive Extended Kalman Filter

<p><strong>Abstract.</strong> The number of applications involving unmanned aerial vehicles (UAVs) grew dramatically during the last decade. Despite such incredible success, the use of drones is still quite limited in GNSS denied environment: indeed, the availability of a reliable GNSS estimates of the drone position is still fundamental in order to enable most of the UAV applications. Given such motivations, in this paper an alternative positioning system for UAVs, based on low cost ultra-wideband band (UWB) is considered. More specifically, this work aims at assessing the positioning accuracy of UWB-based positioning thanks to the comparison with positions provided by a motion capture (MoCap) system. Since the MoCap accuracy is much higher than that of the UWB system, it can be safely used as a reference trajectory for the validation of UWB estimates. In the considered experiment the UWB system allowed to obtain a root mean square error of 39.4&amp;thinsp;cm in 3D positioning based on the use of an adaptive extended Kalman filter, where the measurement noise covariance was adaptively estimated.</p>

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