Fusion of GPS and odometer measurements for map-based vehicle navigation

Lahrech, Abdelkabir; Boucher, Christophe; Noyer, Jean-Charles

doi:10.1109/icit.2004.1490202

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…Considering the limitations of the first 2 methods discussed above and the fact that even techniques such as Kalman filtering could provide accurate results combining the first two approaches [47][48][49], these solutions are not suitable for road sections such as long tunnel stretches. For this reason, the current approach is based on the third alternative.…”

Section: Vehicle Trajectory Estimationmentioning

confidence: 99%

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

et al. 2016

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Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control. This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture, by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level. Considering that the guidance variables are more accurately estimated when dealing with reference points in front of and behind the vehicle, an algorithm based on vehicle dynamics mathematical model is proposed to extend the detected points in cases where the sensor is placed at the front of the vehicle. The algorithm has been tested when driving along a lane delimited by New Jersey barriers at both sides and the results show a correct behaviour. The system is capable of estimating the reference element behind the vehicle with sufficient accuracy when the laser scanner is placed at the front of it, so the robustness of the control input variables (lateral and angular errors) estimation is improved making it unnecessary to place the sensor on the vehicle roof or to introduce additional sensors.

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Section: Vehicle Trajectory Estimationmentioning

confidence: 99%

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

et al. 2016

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“…This issue relates to a different research area which focuses on post-processing the mapmatched data in order to improve along-track vehicle positioning using past and future speed and odometer data. This step can be done on a further stage in the data processing flow, using for example other algorithms based on coupling GNSS absolute positioning with dead reckoning (DR) system, such as odometer and gyroscope, that provides the vehicle's position relative to an initial position (Lahrech et al, 2004) et (Zhao et al, 2003).…”

Section: Performance Of the Algorithmmentioning

confidence: 99%

Map-matching Algorithm for Large Databases

Romon¹,

Bressaud

Lassarre³

et al. 2015

J. Navigation

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This article proposes a batch-mode algorithm to handle the large databases generated from experimentations using probe vehicles. This algorithm can locate raw Global Positioning System (GPS) positions on a map, but can also be used to correct map-matching errors introduced by real time map-matching algorithms. For each journey, the algorithm globally searches for the closest path to the GPS positions, and so is inspired from the "path to path" algorithm's family. It uses the Multiple Hypothesis Technique (MHT) and relies on an innovative weighting system based on the area between the GPS points and the arcs making up the path. For high performance, the algorithm uses an iterative program and the data is stored in tree form. K E Y WO R D S1. Map-matching.

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“…A common solution is to use additional sensors such to overcome the weaknesses of GNSS. In practice, the reliability of vehicle positioning is obtained by the coupling of GNSS that provide absolute positioning, with dead reckoning (DR) system, such as odometer and gyroscope, that provides vehicle's position relative to an initial position [5,6,7]. However, in the long term the performance of DR systems is poor due to the accumulation of measurements errors over time.…”

Section: Introductionmentioning

confidence: 99%

A moving fixed-interval filter/smoother for estimation of vehicle position using odometer and map-matched GPS

Andrieu,

Pierre,

Bressaud

2013

Preprint

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This paper presents some optimal real-time and post-processing estimators of vehicle position using odometer and map-matched GPS measurements. These estimators were based on a simple statistical error model of the odometer and the GPS which makes the model generalizable to other applications. Firstly, an asymptotically minimum variance unbiased estimator and two optimal moving fixed interval filters which are more flexibles are exposed. Then, the post-processing case leads to the construction of two moving fixed interval smoothers. These estimators are tested and compared with the classical Kalman filter with simulated and real data, and the results show a good accuracy of each of them.

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Fusion of GPS and odometer measurements for map-based vehicle navigation

Cited by 11 publications

References 7 publications

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

Map-matching Algorithm for Large Databases

A moving fixed-interval filter/smoother for estimation of vehicle position using odometer and map-matched GPS

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