Evaluation of Shadow Maps for Non-Line-of-Sight Detection in Urban GNSS Vehicle Localization with VANETs - The GAIN Approach

Bauer, Sebastian; Obst, Marcus; Streiter, Robin; Wanielik, Gerd

doi:10.1109/vtcspring.2013.6692555

Cited by 13 publications

(6 citation statements)

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“…The various technologies of GNSS were developed to mitigate the multipath and NLOS effects, which are mainly cataloged by three: antenna-based (33), (34) , receiver-based (35), (36) , and navigation-processor-based used an omnidirectional infrared (IR) camera, which was installed on the roof of vehicle, for estimating the area of sky and identifying NLOS signals (40) . As an extended idea of NLOS exclusion, Bauer et al built a shadow map, which can represent the satellite reception conditions real time (41) . The NLOS measurement can be detected and excluded from the positioning solution (41) .…”

Section: Global Navigation Satellite System (Gnss)mentioning

confidence: 99%

“…As an extended idea of NLOS exclusion, Bauer et al built a shadow map, which can represent the satellite reception conditions real time (41) . The NLOS measurement can be detected and excluded from the positioning solution (41) . Moreover, Obst et al utilized a dynamic 3D map to exclude the potential multipath signal from the observation set for a vehicle-based loosely coupled GNSS/INS integration system (42) .…”

Section: Global Navigation Satellite System (Gnss)mentioning

confidence: 99%

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Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

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Vehicle self-localization is an important and challenging issue in current driving assistance and autonomous driving research activities. This paper mainly investigates two kinds of methods for vehicle self-localization: active sensor based and passive sensor based. Active sensor based localization was firstly proposed for robot localization, and was introduced into autonomous driving recently. The Simultaneous Localization and Mapping (SLAM) techniques is the representative in active sensor based localization. The passive sensor based localization technologies are categorized and explained based on the type of sensors, Global Navigation Satellite System (GNSS), inertial sensors and cameras. In this paper, researches utilizing active sensors and passive sensors for autonomous vehicles are reviewed extensively. Finally, our challenges on self-localization in urban canyon by the system integration of passive sensors is introduced. GNSS error has been improved for the purpose of the self-localization in urban canyon. The performance of the proposed method would suggest possible solution autonomous vehicles which makes use of passive sensors more.

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Section: Global Navigation Satellite System (Gnss)mentioning

confidence: 99%

Section: Global Navigation Satellite System (Gnss)mentioning

confidence: 99%

Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

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“…However, the land vehicle typically has to operate in the urban area, where GNSS positioning accuracy is severely degraded because of the effects of multipath and NonLine-Of-Sight (NLOS). Recently, many approaches of combination GNSS and 3D building map apply multipath and NLOS as additional measurements [1][2][3][4][5]. Our previous studies implemented a ray tracing method to track the transmitting path of GNSS signal [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

SLAM with 3Dimensional-GNSS

Wada

Hsu

et al. 2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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This paper presents a 3Dimensional-GNSS (3D-GNSS) positioning technique, which is used for localization and mapping simultaneously (SLAM). The 3D building model becomes an important aid to many positioning techniques such as LiDAR and GPS positioning methods. In order to automatically create the accurate map in wide area, the precise position of mobile mapping platform is needed. However, GNSS positioning performance is severely degraded because of the effects of multipath and Non-Line-Of-Sight (NLOS) in the urban area. With the aid of 3D building map, the proposed 3D-GNSS distinguishes whether the received GNSS signal is transmitted as LOS or NLOS path, and can calculate the length of reflection path for the improvement of positioning error. To achieve highly accurate positioning in urban canyon, we further develop 3D-GNSS based integrated vehicle self-localization system, which is comprised of 3D-GNSS, inertial sensor and vision sensor. On the other hand, highly precise positioning needs the accurate 3D building map. Inspired by the idea of 3D-GNSS, this paper proposes to optimize the 3D building map by estimating the reflection of GNSS signals. In addition, the position of mobile mapping platform is estimated from our integrated localization system in the mapping stage. The experimental result demonstrates that sub-meter accuracy is achieved in both localization and mapping.

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“…Marais et al , proposed to exclude the satellites located in non-sky regions for localization using the omnidirectional fisheye camera [ 12 ]. In addition, 3D building model, shadow mapping, and omnidirectional infrared (IR) cameras were employed for excluding the unhealthy signals and mitigating the effects of NLOS and multipath [ 13 , 14 , 15 ]. However, the exclusion of satellites will cause the distortion of Horizontal Dilution of Precision (HDOP).…”

Section: Introductionmentioning

confidence: 99%

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment

Hsu

Kamijo

2015

Sensors

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This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error.

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Evaluation of Shadow Maps for Non-Line-of-Sight Detection in Urban GNSS Vehicle Localization with VANETs - The GAIN Approach

Cited by 13 publications

References 10 publications

Autonomous Vehicle Technologies :Localization and Mapping

Autonomous Vehicle Technologies :Localization and Mapping

SLAM with 3Dimensional-GNSS

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment

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