Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

Nedevschi, Sergiu; Popescu, Víctor; Danescu, Radu; Mariţa, Tiberiu; Oniga, Florin

doi:10.1109/tits.2012.2228191

Cited by 77 publications

(56 citation statements)

References 33 publications

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“…Marita et al detected the stop line and cross road from stereo camera, and using the depth information to do the localization at the intersection scenarios (57) . Moreover, the arrow mark recognition was also proposed for localization at intersection (58), (59) .…”

Section: (A) (B) (C)mentioning

confidence: 99%

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: (A) (B) (C)mentioning

confidence: 99%

Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

View full text Add to dashboard Cite

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“…The ego-localization problem is one shared by automotive applications [5], [6], [8], and robotics [1], [10] where it is more often posed as the Simultaneous Localization and Mapping (SLAM) problem [11]. SLAM has been a very active research area in the robotics community where unknown environments must be mapped as the robot is localized within the dynamically updated map.…”

Section: Related Workmentioning

confidence: 99%

“…For automotive ego-localization, similar monocular methods have been employed which separate the mapping step from localization by using a pre-constructed database [5], [8] or image databases such as Google Street View [14]. These methods perform complete localization relative to the database images, which can enable high accuracy-for example, up to 10 cm precision when combined with an IMU [5].…”

Section: Related Workmentioning

confidence: 99%

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Single camera vehicle localization using SURF scale and dynamic time warping

Wong

Deguchi

Ide

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-Vehicle ego-localization is an essential process for many driver assistance and autonomous driving systems. The traditional solution of GPS localization is often unreliable in urban environments where tall buildings can cause shadowing of the satellite signal and multipath propagation. Typical visual feature based localization methods rely on calculation of the fundamental matrix which can be unstable when the baseline is small.In this paper we propose a novel method which uses the scale of matched SURF image features and Dynamic Time Warping to perform stable localization. By comparing SURF feature scales between input images and a pre-constructed database, stable localization is achieved without the need to calculate the fundamental matrix. In addition, 3D information is added to the database feature points in order to perform lateral localization, and therefore lane recognition.From experimental data captured from real traffic environments, we show how the proposed system can provide high localization accuracy relative to an image database, and can also perform lateral localization to recognize the vehicle's current lane.

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“…Although these methods can detect lanes well in the case of slight occlusion, they are less feasible in the situation of traffic congestion, and they are not able to estimate the direction of the lanes either. Nedevschi et al [18] proposed an ego-localization method using landmarks at the intersections, but it fails when the landmark is covered by the crowded vehicles.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Yuan

Chen

Zhao³

et al. 2017

J. Comput. Sci. Technol.

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With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

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Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

Cited by 77 publications

References 33 publications

Autonomous Vehicle Technologies :Localization and Mapping

Autonomous Vehicle Technologies :Localization and Mapping

Single camera vehicle localization using SURF scale and dynamic time warping

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

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