Ego-localization using streetscape image sequences from in-vehicle cameras

Uchiyama, Hiroyuki; Deguchi, Daisuke; Takahashi, Tomokazu; Ide, Ichiro; Murase, Hiroshi

doi:10.1109/ivs.2009.5164275

Cited by 18 publications

(21 citation statements)

References 10 publications

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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%

“…They also usually employ supporting sensors to achieve localization-either an IMU [5] or odometry information [14]. A simpler approach is to localize against the closest database image, of known location, using DTW (or Dynamic Programming) to remove temporal differences between input and database image streams [6], [15]. The matching between sample images and those in the database can be performed by using a kind of template matching [15], or by using a low bit-rate image sequence instead of single images [16], which is stable in varying environments but does not provide high localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…There is an increasing number of systems that use a preconstructed database which is then localized against [5], [6], [7], [8]. Most current systems either use several cameras or a variety of sensors for localization, or are unable to perform lateral positioning so can not recognize which lane is currently being used by the vehicle.…”

Section: Introductionmentioning

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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Section: Related Workmentioning

confidence: 99%

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

Self Cite

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“…High-precision localization using cameras can re-duce the system cost significantly, but this usually needs high-precision digital maps [9][10][11] or preconstructed image databases for matching [12][13] . Uchiyama et al [12] achieved the ego-localization using a matching database and a binocular camera, where the synchronization and the stereo matching of binocular vision are too difficult to guarantee the positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Uchiyama et al [12] achieved the ego-localization using a matching database and a binocular camera, where the synchronization and the stereo matching of binocular vision are too difficult to guarantee the positioning accuracy. Wong et al [13] used an on-board monocular camera and an image sequence database to determine the actual location of the vehicle on road.…”

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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Vision-Based Vehicle Localization Using a Visual Street Map with Embedded SURF Scale

Wong

Deguchi

Ide

et al. 2015

Lecture Notes in Computer Science

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Accurate vehicle positioning is important not only for in-car navigation systems but is also a requirement for emerging autonomous driving methods. Consumer level GPS are inaccurate in a number of driving environments such as in tunnels or areas where tall buildings cause satellite shadowing. Current vision-based methods typically rely on the integration of multiple sensors or fundamental matrix calculation which can be unstable when the baseline is small. In this paper we present a novel visual localization method which uses a visual street map and extracted SURF image features. By monitoring the difference in scale of features matched between input images and the visual street map within a Dynamic Time Warping framework, stable localization in the direction of motion is achieved without calculation of the fundamental or essential matrices. We present the system performance in real traffic environments. By comparing localization results with a high accuracy GPS ground truth, we demonstrate that accurate vehicle positioning is achieved.

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Ego-localization using streetscape image sequences from in-vehicle cameras

Cited by 18 publications

References 10 publications

Single camera vehicle localization using SURF scale and dynamic time warping

Single camera vehicle localization using SURF scale and dynamic time warping

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

Vision-Based Vehicle Localization Using a Visual Street Map with Embedded SURF Scale

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