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

Gu, Yanlei; Hsu, Li‐Ta; Kamijo, Shunsuke

doi:10.3390/s151229795

Cited by 45 publications

(43 citation statements)

References 49 publications

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“…where denotes the pseudorange measurement, denotes the observation matrix, which consists of the unit LOS vector between the satellite and assumed receiver position, and is the receiver states, shown in (2). is an error term and shown in (3).…”

Section: Fault Detection Based On Consistency Checkmentioning

confidence: 99%

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Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

et al. 2017

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Abstract-Sensors play important roles for autonomous driving.Localization is definitely a key one. Undoubtedly, global positioning system (GPS) sensor will provide absolute localization for almost all the future land vehicles. In terms of driverless car, 1.5 meters of positioning accuracy is the minimum requirement since the vehicle has to keep in a driving lane that usually wider than 3 meters. However, the skyscrapers in highly-urbanized cities such as Tokyo and Hong Kong, dramatically deteriorate GPS localization performance, leading more than 50 meters of error. GPS signals are reflected at modern glassy buildings which caused the notorious multipath effect. Fortunately, the number of navigation satellite is rapidly increasing in a global scale since the rise of multi-GNSS (global navigation satellite system). It provides an excellent opportunity for positioning algorithm developer of GPS sensor. More satellites in the sky implies more measurements to be received. Novelty, this paper proposes to take advantage of the fact that clean measurements (refers to line-of-sight measurement) are consistent and multipath measurements are inconsistent. Based on this consistency check, the faulty measurements can be detected and excluded to obtain better localization accuracy. Experimental results indicate that the proposed method can achieve less than 1 meter lateral positioning error in middle urban canyons.

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Section: Fault Detection Based On Consistency Checkmentioning

confidence: 99%

“…utonomous driving requires accurate locating service provided by sensors in all environments [1,2]. Global positioning system (GPS) sensor provides absolute localization service in all outdoor areas in all weather conditions [3].…”

Section: Introductionmentioning

confidence: 99%

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

et al. 2017

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“…These marking information there is able to aid the integration system [13][14][15]. In our previous work, we focused on the problem of vehicle self-localization in the most challenging environment, a city urban environment, and improves the positioning performance by integrating 3D-GNSS, inertial sensors and vision based lane detection [16]. This paper utilizes our integrated localization system to provide the position of mobile mapping platform for mapping.…”

Section: Introductionmentioning

confidence: 95%

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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“…The definition of satellite visibility is the number of acquired satellites dividing the expected number of satellites in view as (7). …”

Section: A Vehicle On-under Viaduct Determination Modelmentioning

confidence: 99%

“…additional costs [7,8]. For current automobiles, a cost-effective solution would be a standalone GPS navigator, which consists of a GPS receiver and road map information.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Hsu

Kamijo

2017

IEEE Trans. Intell. Veh.

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Abstract-The rapid development of road networks in highly urbanized cities requires a substantial number of viaducts to reduce the increasing traffic burden on urban highways. Unfortunately, this road design deteriorates the performance of global positioning system (GPS) navigators due to the signal blockage between satellite and receiver. As a result, it is difficult for GPS navigators to determine whether the vehicle is driving on the street or viaduct. Misleading information could confuse drivers and lead them to drive irregularly, which is dangerous in heavy traffic. This paper proposes a novel classification algorithm based on the fact that different satellite signals and conditions can be observed in the on-street and on-viaduct cases by the implementation of dynamic Bayesian network (DBN) to distinguish the driving area of a vehicle. In addition, the proposed DBN can also accurately estimate the driving altitude of the vehicle according to the experiment results.

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

Cited by 45 publications

References 49 publications

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

SLAM with 3Dimensional-GNSS

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

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