Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions
Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accurac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Choi, Jungsu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Control, Automation and Systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 15(2017), 6 vom: Dez., Seite 2859-2870 |
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| Übergeordnetes Werk: |
volume:15 ; year:2017 ; number:6 ; month:12 ; pages:2859-2870 |
| Links: |
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| DOI / URN: |
10.1007/s12555-016-0294-1 |
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| 520 | |a Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. | ||
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10.1007/s12555-016-0294-1 doi (DE-627)SPR026435438 (SPR)s12555-016-0294-1-e DE-627 ger DE-627 rakwb eng Choi, Jungsu verfasserin aut Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. Extended Kalman filter (dpeaa)DE-He213 motion sensors (dpeaa)DE-He213 self-driving vehicle (dpeaa)DE-He213 sensor fusion (dpeaa)DE-He213 Kong, Kyoungchul aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 6 vom: Dez., Seite 2859-2870 (DE-627)SPR026303256 nnns volume:15 year:2017 number:6 month:12 pages:2859-2870 https://dx.doi.org/10.1007/s12555-016-0294-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 6 12 2859-2870 |
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10.1007/s12555-016-0294-1 doi (DE-627)SPR026435438 (SPR)s12555-016-0294-1-e DE-627 ger DE-627 rakwb eng Choi, Jungsu verfasserin aut Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. Extended Kalman filter (dpeaa)DE-He213 motion sensors (dpeaa)DE-He213 self-driving vehicle (dpeaa)DE-He213 sensor fusion (dpeaa)DE-He213 Kong, Kyoungchul aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 6 vom: Dez., Seite 2859-2870 (DE-627)SPR026303256 nnns volume:15 year:2017 number:6 month:12 pages:2859-2870 https://dx.doi.org/10.1007/s12555-016-0294-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 6 12 2859-2870 |
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10.1007/s12555-016-0294-1 doi (DE-627)SPR026435438 (SPR)s12555-016-0294-1-e DE-627 ger DE-627 rakwb eng Choi, Jungsu verfasserin aut Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. Extended Kalman filter (dpeaa)DE-He213 motion sensors (dpeaa)DE-He213 self-driving vehicle (dpeaa)DE-He213 sensor fusion (dpeaa)DE-He213 Kong, Kyoungchul aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 6 vom: Dez., Seite 2859-2870 (DE-627)SPR026303256 nnns volume:15 year:2017 number:6 month:12 pages:2859-2870 https://dx.doi.org/10.1007/s12555-016-0294-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 6 12 2859-2870 |
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10.1007/s12555-016-0294-1 doi (DE-627)SPR026435438 (SPR)s12555-016-0294-1-e DE-627 ger DE-627 rakwb eng Choi, Jungsu verfasserin aut Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. Extended Kalman filter (dpeaa)DE-He213 motion sensors (dpeaa)DE-He213 self-driving vehicle (dpeaa)DE-He213 sensor fusion (dpeaa)DE-He213 Kong, Kyoungchul aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 6 vom: Dez., Seite 2859-2870 (DE-627)SPR026303256 nnns volume:15 year:2017 number:6 month:12 pages:2859-2870 https://dx.doi.org/10.1007/s12555-016-0294-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 6 12 2859-2870 |
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10.1007/s12555-016-0294-1 doi (DE-627)SPR026435438 (SPR)s12555-016-0294-1-e DE-627 ger DE-627 rakwb eng Choi, Jungsu verfasserin aut Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. Extended Kalman filter (dpeaa)DE-He213 motion sensors (dpeaa)DE-He213 self-driving vehicle (dpeaa)DE-He213 sensor fusion (dpeaa)DE-He213 Kong, Kyoungchul aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 6 vom: Dez., Seite 2859-2870 (DE-627)SPR026303256 nnns volume:15 year:2017 number:6 month:12 pages:2859-2870 https://dx.doi.org/10.1007/s12555-016-0294-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 6 12 2859-2870 |
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Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions |
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Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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Abstract Real-time estimation of the absolute position and orientation plays a critical role in the control and path planning of a self-driving vehicle. Global positioning systems (GPSs) are used to estimate the absolute position of a vehicle from satellite signals, but their reliability and accuracy are not sufficient for precise control. An inertial navigation system (INS) is often used to complement the GPS, but it is always subjected to a drift problem due to integration. Such a problem has been solved by utilizing a high performance GPS, an INS, a vision sensor, a radar, a laser scanner, or their combination. However, these sensor systems are very expensive compared to the price of common commercial vehicles, which hinders the popularization of self-driving vehicles. Moreover, the target users of self-driving vehicles are most likely the elderly, as well as the blind, and thus the price is an important factor in the system design. In this paper, a sensor fusion method for a low-price self-driving vehicle and real-time path planning algorithm without a laser scanner are introduced. The proposed low-price sensor system consists of a GPS, an inertial measurement unit (IMU) with a three-axes accelerometer, a three-axes gyroscope, and a three-axes magnetometer, an encoder at the real wheels, and a potentiometer that measures the steering angle. The overall price is less than 300 USD. For the complete estimation of the absolute position and orientation of the vehicle, the proposed method estimates the driving velocity using a kinematic Kalman filter, then the orientation angle and absolute position are calculated by the recursive least squares method. The obtained information is then used for the real-time control of a self-driving vehicle. The proposed method is verified through simulation studies and by experimental results. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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