Vehicle localization using mono-camera and geo-referenced traffic signs

Qu, Xiaozhi; Soheilian, Bahman; Paparoditis, Nicolas

doi:10.1109/ivs.2015.7225751

Cited by 72 publications

(45 citation statements)

References 26 publications

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“…Other than Street View imagery, in [207], the authors show that it is possible to consider traffic signs as geo-referenced landmarks coming from existing maps. 3D models of these traffic signs are matched in images and the position of the vehicle is optimized inside a Bundle Adjustment approach.…”

Section: Localization In Existing Mapsmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Localization In Existing Mapsmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

366

View full text Add to dashboard Cite

show abstract

“…The lane markings are then detected online using a stereo camera, and matched against the ones in the map. Welzel et al [37] and Qu et al [28] utilize traffic signs to assist image-based vehicle localization. Specifically, traffic signs are detected from images and matched against a geo-referenced sign database, after which local bundle adjustment is conducted to estimate a fine-grained pose.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Urtasun

Tartavull

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry-and LiDAR intensitybased localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensitybased approaches.

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“…• Itinerary computations and public building accessibility for soft mobilities (disabled, strollers, ...) (Serna and Marcotegui, 2013) • Mobile mapping registration on aerial images (Tournaire et al, 2006) • Image based localization using ground landmarks (Qu et al, 2015) • Water flow simulations…”

Section: Contextmentioning

confidence: 99%

Road Orthophoto/DTM Generation From Mobile Laser Scanning

Vallet¹,

Papelard²

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper proposes a pipeline to produce road orthophoto and DTM from Mobile Laser Scanning (MLS). For the ortho, modern laser scanners provide a reflectance information allowing for high quality grayscale images, at a much finer resolution than aerial photography can offer. For DTM, MLS offers a much higher accuracy and density than aerial products. This increased precision and resolution leverages new applications for both ortho and DEM. The first task is to filter ground vs non ground, then an interpolation is conducted to build image tiles from the filtered points. Finally, multiple layers are registered and blended to allow for seamless fusion. Our proposed approach achieves high quality products and scaling up is demonstrated.

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Vehicle localization using mono-camera and geo-referenced traffic signs

Cited by 72 publications

References 26 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Road Orthophoto/DTM Generation From Mobile Laser Scanning

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