Positioning of vehicle on undulating ground using GPS and dead reckoning

Aono, Toshihiro; Fujii, Kenjiro; Hatsumoto, S.; Kamiya, T.

doi:10.1109/robot.1998.680970

Cited by 39 publications

(23 citation statements)

References 2 publications

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“…In Aono et al (1998) a method of positioning a vehicle on undulating ground by fusing DGPS data and motion sensor data is proposed. A fibre optic gyro, a roll pitch sensor, and wheel encoders are used as motion sensors.…”

Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 99%

“…A number of localization techniques have been developed to serve a variety of applications Al-Bayari & Sadoun (2005); Aono et al (1998); Bouju et al (2002); Cramer (1997); Dao et al (2002); Drawil & Basir (2010) ;; Lai & Tsai (2003); Nishimura et al (1996); Sliety (2007); Stockus et al (2000). In recent years, the focus has been on localization accuracy improvement -an issue considered crucial, specially in mission critical applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emerging New Trends in Hybrid Vehicle Localization Systems

Drawil¹,

Basir²

2012

Global Navigation Satellite Systems: Signal, Theory and Applications

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Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emerging New Trends in Hybrid Vehicle Localization Systems

Drawil¹,

Basir²

2012

Global Navigation Satellite Systems: Signal, Theory and Applications

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“…Therefore, the following equations can be got: is the coordinates of the marker in the global coordinate system. According to (3), the nonlinear measurement model can be defined as following:…”

Section: Magnetic Sensing Systemmentioning

confidence: 99%

“…The vehicle must be equipped with external sensors that obtain information from the environment to help the vehicle determine its location more accurately. Most researchers utilized GPS, ultrasonic [1][2][3] , optical flow sensor or machine vision based systems to provide the positioning information. The precision or reliability of results can be improved with several sensors [4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Improved Intelligent Vehicle Localization Using Magnetic Ruler

Wang¹,

Yang²,

Wang³

et al. 2011

IJCIS

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This paper proposes an improved intelligent vehicle localization method, which integrates the data from the odometry and the magnetic ruler(MR). The MR is used to detect the magnetic markers, which provides the absolute positioning reference. The data from odometry and the MR are used to localize the vehicle by using unscented Kalman filter. The data association (DA) method is adopted to validate the observations from the MR. The experiment results show that the method is a robust way.

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“…For localization, dead reckoning has always been attractive because of its pervasiveness [4]- [6]. With the rapid development of technology, GPS receivers are quickly becoming the sensor of choice for outdoor localization [7]- [9]. Imaging sensors, such as CCD cameras and laser range finders, have also become very popular mobile robot components [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Localization Methods for a Mobile Robot in Urban Environments

2004

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This paper addresses the problems of designing, building and using mobile robots for urban site modeling. It presents work on both system and algorithmic aspects. On the system level, we have designed and built a functioning autonomous mobile robot. The design extends an existing robotic vehicle with a sensor suite consisting of a digital compass with an integrated inclinometer, a global positioning unit, and a camera mounted on a pan-tilt head. The system is controlled by a distributed software architecture for mobile robot navigation and site modeling. On the algorithmic level, we have developed a localization system that employs two methods. The first method uses odometry, the compass module and the global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with realworld experiments.

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Positioning of vehicle on undulating ground using GPS and dead reckoning

Cited by 39 publications

References 2 publications

Emerging New Trends in Hybrid Vehicle Localization Systems

Emerging New Trends in Hybrid Vehicle Localization Systems

Improved Intelligent Vehicle Localization Using Magnetic Ruler

Localization Methods for a Mobile Robot in Urban Environments

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