Single camera vehicle localization using SURF scale and dynamic time warping

Wong, David; Deguchi, Daisuke; Ide, Ichiro; Murase, Hiroshi

doi:10.1109/ivs.2014.6856545

Cited by 10 publications

(11 citation statements)

References 19 publications

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“…The problem of localization of a vehicle, aerial and ground, in urban and indoor environments where GPS signals are not always reliable is well studied in the literature. In particular, authors in [8] used techniques in computer vision to address the problem. Numerous variants of Simultaneous Localization and Mapping (SLAM) techniques have also been developed for high precision vehicle localization [9], [10].…”

Section: Related Workmentioning

confidence: 99%

Routing unmanned vehicles in GPS-denied environments

Sundar

Misra

Rathinam

et al. 2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in a GPSrestricted environment while enabling localization for the vehicle. We also develop algorithms to compute optimal paths for the vehicle using the proposed formulation. Extensive simulation results are also presented to corroborate the effectiveness and performance of the proposed formulation and algorithms.

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Section: Related Workmentioning

confidence: 99%

Routing unmanned vehicles in GPS-denied environments

Sundar

Misra

Rathinam

et al. 2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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show abstract

“…Image databases for visual localization typically store descriptor representations for each image [2], [3], and featurebased methods will contain a set of many feature descriptors per database image [6], [24]. Since feature-based localization methods perform feature matching between database and localization images, a carefully constructed database of known inliers is important for better matching performance.…”

Section: Pre-matched Databasementioning

confidence: 99%

“…In addition, calculation of the essential matrix when the distance between the query and database images is small, can cause short baseline degeneracies [5]. Our previous work [6], [7] uses scale invariant features and compares the scale of corresponding query and database image features to determine a database image match. If two images have the same viewing direction, their corresponding feature points will have a similar scale when the capture positions were spatially close.…”

Section: Introductionmentioning

confidence: 99%

Single Camera Vehicle Localization Using Feature Scale Tracklets

Wong

Deguchi

Ide

et al. 2017

IEICE Trans. Fundamentals

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Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal. In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database. Image feature points are extracted, but the calculation of camera poses is not required -instead we make use of the feature points' scale. For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into "tracklets". These "Feature scale tracklets" are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

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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. Their algorithm relies on a large and complex database, including the scenes along and around the 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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Single camera vehicle localization using SURF scale and dynamic time warping

Cited by 10 publications

References 19 publications

Routing unmanned vehicles in GPS-denied environments

Routing unmanned vehicles in GPS-denied environments

Single Camera Vehicle Localization Using Feature Scale Tracklets

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

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