Enhancement of global vehicle localization using navigable road maps and dead-reckoning

Fouque, Clément; Bonnifait, Philippe; Betaille, David

doi:10.1109/plans.2008.4570082

Cited by 34 publications

(18 citation statements)

References 7 publications

(7 reference statements)

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“…In [5], Najjar et al propose a road-matching localization algorithm, using Belief Theory for road selection and Kalman Filtering for recursive estimation. Some other similar studies can be found in [7], [6]. These road-matching algorithms achieve good localization in a global fashion.…”

Section: Introductionmentioning

confidence: 71%

Curb-intersection feature based Monte Carlo Localization on urban roads

Qin

Chong

Bandyopadhyay

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of "Virtual LIDAR" to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians.

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Section: Introductionmentioning

confidence: 71%

Curb-intersection feature based Monte Carlo Localization on urban roads

Qin

Chong

Bandyopadhyay

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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“…International Journal of Navigation and Observation 5 to the MM problem that have been researched [26,27]. This paper focuses on three main algorithms from [31].…”

Section: Map Matchingmentioning

confidence: 99%

“…Map matching (MM) is the process of utilizing a digital road network map database to improve the predicted position errors during integration [25][26][27][28][29]. Motivated by the simplicity and drawbacks of KF, this research will focus on reducing the KF integration errors by utilizing MM.…”

Section: Introductionmentioning

confidence: 99%

Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

Cossaboom

Georgy

Karamat

et al. 2012

International Journal of Navigation and Observation

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Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system-(MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle's built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.

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“…Studies in [2], [4], [5] make use of environment static map to improve SLAM matching confidence. A network of cooperating vehicles is explored in [6], [7] with the aim to improve localization of each vehicle.…”

Section: Introductionmentioning

confidence: 99%

Improving poor GPS area localization for intelligent vehicles

Nguyen¹,

Nashashibi²,

Dao³

et al. 2017

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Abstract-Precise positioning plays a key role in successful navigation of autonomous vehicles. A fusion architecture of Global Positioning System (GPS) and Laser-SLAM (Simultaneous Localization and Mapping) is widely adopted. While Laser-SLAM is known for its highly accurate localization, GPS is still required to overcome accumulated error and give SLAM a required reference coordinate. However, there are multiple cases where GPS signal quality is too low or not available such as in multi-story parking, tunnel or urban area due to multipath propagation issue etc. This paper proposes an alternative approach for these areas with WiFi Fingerprinting technique to replace GPS. Result obtained from WiFi Fingerprinting will then be fused with Laser-SLAM to maintain the general architecture, allow seamless adaptation of vehicle to the environment.

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Enhancement of global vehicle localization using navigable road maps and dead-reckoning

Cited by 34 publications

References 7 publications

Curb-intersection feature based Monte Carlo Localization on urban roads

Curb-intersection feature based Monte Carlo Localization on urban roads

Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

Improving poor GPS area localization for intelligent vehicles

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