scholarly journals Vehicle Position Estimation Using Low-cost RTK Module, Wheelpulse, and IMU Sensor

2018 ◽  
Vol 26 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Jongyoon Yee ◽  
Taehong Kim ◽  
Hyunwoo Kim
Actuators ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Niklas König ◽  
Matthias Nienhaus ◽  
Emanuele Grasso

Techniques for estimating the plunger position have successfully proven to support operation and monitoring of electromagnetic actuators without the necessity of additional sensors. Sophisticated techniques in this field make use of an oversampled measurement of the rippled driving current in order to reconstruct the position. However, oversampling algorithms place high demands on AD converters and require significant computational effort which are not desirable in low-cost actuation systems. Moreover, such low-cost actuators are affected by eddy currents and parasitic capacitances, which influence the current ripple significantly. Therefore, in this work, those current ripples are modeled and analyzed extensively taking into account those effects. The Integrator-Based Direct Inductance Measurement (IDIM) technique, used for processing the current ripples, is presented and compared experimentally to an oversampling technique in terms of noise robustness and implementation effort. A practical use case scenario in terms of a sensorless end-position detection for a switching solenoid is discussed and evaluated. The obtained results prove that the IDIM technique outperforms oversampling algorithms under certain conditions in terms of noise robustness, thereby requiring less sampling and calculation effort. The IDIM technique is shown to provide a robust position estimation in low-cost applications as in the presented example involving a end-position detection.


Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.


GPS Solutions ◽  
2017 ◽  
Vol 21 (3) ◽  
pp. 1379-1387 ◽  
Author(s):  
Sanat K. Biswas ◽  
Li Qiao ◽  
Andrew G. Dempster

Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen

For a semi-autonomous or fully-autonomous parking system, detecting adequate parking spot is the first step. Ultrasonic sensor possesses a good compromise between cost and performance since the detection range is very small. This paper describes a parking assist system with two ultrasonic sensors mounted at the left front and right front corners of the vehicle. Special signal filtering and processing is derived. Kinematic observer for the vehicle position estimation during search and parking phases is discussed. The suggested algorithm is implemented in Matlab/Simulink and was verified in a test vehicle.


Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.


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