Range-Doppler Registration

Pishehvari, Ahmad; Iurgel, U.; Stefer, Markus; Tibken, Bernd

doi:10.1109/iccre49379.2020.9096494

Cited by 2 publications

(1 citation statement)

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“…Precise navigation is one of the most important requirements of a fully autonomous car. The car should possess the knowledge of its own position and orientation, in order to navigate precisely within and through an environment [ 1 ]. Current road cars use the Global Positioning System (GPS) [ 2 , 3 , 4 ], an example of the Global Navigation Satellite System (GNSS) which aids the car for estimating its own position.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments

Swaminathan

Sommer

Becker

et al. 2022

Sensors

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Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.

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Section: Introductionmentioning

confidence: 99%