iGPS: Global Positioning in Urban Canyons with Road Surface Maps

Drevelle, Vincent; Bonnifait, Philippe

doi:10.1109/mits.2012.2203222

Cited by 18 publications

(11 citation statements)

References 16 publications

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“…We report results obtained with the same dataset as that used in [19]. The test starts with good satellite visibility, before entering urban canyons with 2 or 3 satellites in view.…”

Section: B Resultsmentioning

confidence: 99%

Reliable Positioning Domain Computation for Urban Navigation

Drevelle

Bonnifait

2013

IEEE Intell. Transport. Syst. Mag.

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Abstract-Reliable positioning is a key issue for intelligent vehicle navigation. Interval-based positioning methods have shown to be capable of computing relevant confidence domains used for integrity monitoring in environments which are challenging for Global Positioning System (GPS). The approach presented in this paper consists in tightly coupling a GPS receiver with a 3D-map of the drivable area. Interval analysis is employed to solve the constraint positioning problem using contractions and bisections. Integrity is provided through the use of a robust set-inversion scheme applied to a redundant measurement set. If the prior distribution of the measurement noise is known, it is possible to compute confidence domains that correspond to a given integrity risk, which is often set very low out of safety considerations. In this paper we examine a way of validating the proposed approach, using a real experimental dataset and a ground truth equipment. Different tunings of the method, corresponding to different risks, are assessed in terms of availability and integrity in order to compute statistical metrics. Results indicate that this methodology is relevant since the specified risk corresponds to experimental observations.

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“…We report results obtained with the same dataset as that used in [19]. The test starts with good satellite visibility, before entering urban canyons with 2 or 3 satellites in view.…”

Section: B Resultsmentioning

confidence: 99%

Reliable Positioning Domain Computation for Urban Navigation

Drevelle

Bonnifait

2013

IEEE Intell. Transport. Syst. Mag.

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“…To reduce positioning ambiguities and limit the number of road facets used for contraction, a road-topology aware facet selection step can be implemented before calling the SIVIA algorithm [26]. Similarly to a map-matching algorithm, the topological facet selection step makes use of facet connections, covered distance, and prior position to determine the candidate triangles of the map.…”

Section: Position Computationmentioning

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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“…• 3D building model -this level encompasses all approaches based on 3D building models which do not rely on ray tracing or shadow matching: [4], [22], [71], [81], [82], [83].…”

Section: Identificationmentioning

confidence: 99%

“…Thus, building boundary data, digital terrain models and building models were used. [8], [14], [33], [50], [80], [98], [99] [4], [14], [30], [33], [35], [92], [47], [48], [50], [51], [58], [67], [68], [70], [74], [81], [82], [83], [85] Via person [18], [22] [43], [49], [66], [71] (No information) [28], [29], [31], [32], [34], [37], [73], [84], [86], [87], [88] Simulation Static measurement…”

Section: Recent Solutions For Positioning Under Nlos Conditionsmentioning

confidence: 99%