Localization for intelligent vehicle by fusing mono-camera, low-cost GPS and map data

Li, Hao; Nashashibi, Fawzi; Toulminet, G.

doi:10.1109/itsc.2010.5625240

Cited by 43 publications

(25 citation statements)

References 16 publications

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“…When beacons are passive, the sensor measurement association problem is easy to address with highly discernible landmarks. Examples in computer vision systems applied to outdoor navigation are the use of natural sparse features [4] or road signs [5].…”

Section: Introductionmentioning

confidence: 99%

Localization Confidence Domains via Set Inversion on Short-Term Trajectory

Drevelle,

Bonnifait

2013

IEEE Trans. Robot.

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International audienceThe knowledge of localization uncertainties is of prime importance when the navigation of intelligent vehicles has to deal with safety issues. This paper presents a robust estimation method that is able to quantify the localization confidence based on interval analysis and constraint propagation. First, tightly coupled position domains are computed by constraint propagation on Global Positioning System (GPS) measurements and a precise 3-D map of the drivable area. Since GPS is prone to satellite masking and wrong measurements in urban areas, a second stage provides localization integrity and information availability by the use of a position and proprioceptive data history. A robust constraint propagation algorithm is employed to compute the current vehicle pose. It is able to handle erroneous positions with a chosen integrity risk. Experiments carried out in urban canyons illustrate the performance of the method in comparison with a particle filter. Despite bad satellite visibility, full positioning availability is obtained, and errors are less than 5.1 m during 95% of the trial. In opposition to the particle filter, confidence domains are consistent with ground truth, which confirms the high integrity of the method

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Section: Introductionmentioning

confidence: 99%

Localization Confidence Domains via Set Inversion on Short-Term Trajectory

Drevelle,

Bonnifait

2013

IEEE Trans. Robot.

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“…Recent approaches augment standard navigation maps by additional information about lane markings [9], signs [10], stop lines [11] or a generally enhanced road geometry [12]. The relative location to these landmarks can then be included in the positioning process.…”

Section: B Related Workmentioning

confidence: 99%

“…[15], [10], [16], [1], [9]) that represent the vehicle position and orientation jointly in two-dimensional Cartesian coordinates, this work represents the desired pose separately as onedimensional longitudinal position and one-dimensional orientation to the digital map. The longitudinal position describes the driven distance along the digital road map relative to the starting point of the map.…”

Section: Factorized Self-localizationmentioning

confidence: 99%

Feature evaluation of factorized self-localization

Schule

Büchner²,

Schweiger

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Localization on a digital map is a crucial point for many advanced driver assistance systems that make use of digital map data. State of the art work mainly relies on inaccurate GPS measurements, special and costly sensors or especially attributed digital maps. In contrast to that, this work presents a localization algorithm with in-vehicle available sensors and a standard navigation map. A factorized state estimation that computes position and orientation with separate features allows accurate and reliable positioning. This work thereto proposes correlationbased features that match road curvatures of digital map and sensor data for position estimation. The vehicle orientation on the digital map can then be computed by either matching an egomotion trajectory to the digital map road geometry or by using an optical lane recognition system. Sensitive parameters of this approach are thoroughly evaluated with a highly precise DGPS reference system.

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“…2, the robot position at time k can be predicted from the geometric analysis of the robot movement using the position at time k − 1 (x k−1 ), the distance traveled (D k ) and rotation angles (Δθ 1k , Δθ 2k ), and the inclination angle (α). At this point, the robot uses the motion model with adaptive parameter D k cos α for nonflat road instead of the distance traveled (D k ) in (10). The uncertainty level of predicted position is expressed by the covariance in (11).…”

Section: B Prediction Stepmentioning

confidence: 99%

“…Recent work has shown the possibility of robot localization by fusing camera, GPS, and map data [10]. Their basic idea is that robot positions are determined based on PF fusing the traffic sign data detected by camera, GPS, and map data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive localization for mobile robots in urban environments using low-cost sensors and enhanced topological map

Lee

Christiand²,

et al. 2011

2011 15th International Conference on Advanced Robotics (ICAR)

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This paper presents a technique for accurate localization of mobile robots using an enhanced topological map and using the low-cost sensors such as wheel odometer, global positioning system (GPS), and mono-camera. The localization framework is based on EKF to fuse the sensor data and the topological map. The sensor data include the positions of traffic marks measured by camera and topological map having the actual positions of traffic marks extracted from aerial or satellite images in advance. Our approach obtains the adaptive parameter for EKF localization by matching two positions, measured by camera and extracted from topological map, on each traffic mark. The adaptive parameter reflects the geographical characteristics, e.g. hill, corner, and road surfaces. The proposed method has shown high accuracy result and apparently better performance of the EKF localization with adaptive parameter. The proposed method is economically feasible and practically applicable to commercial robots using the low-cost sensors and providing the reliable localization services.

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Localization for intelligent vehicle by fusing mono-camera, low-cost GPS and map data

Cited by 43 publications

References 16 publications

Localization Confidence Domains via Set Inversion on Short-Term Trajectory

Localization Confidence Domains via Set Inversion on Short-Term Trajectory

Feature evaluation of factorized self-localization

Adaptive localization for mobile robots in urban environments using low-cost sensors and enhanced topological map

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