MAV urban localization from Google street view data

Majdik, András; Albers-Schoenberg, Yves; Scaramuzza, Davide

doi:10.1109/iros.2013.6696925

Cited by 82 publications

(50 citation statements)

References 26 publications

(34 reference statements)

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“…In [194], an aerial vehicle takes advantage of Street Views to localize itself. Artificial views are created to overcome the difference in viewpoints and are then compared using the Approximate Nearest Neighbors on extracted features.…”

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.

673

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.

673

366

View full text Add to dashboard Cite

show abstract

“…Nowadays, several sources of georeferenced in situ images are available and accessible to the public, although under diverse types of licensing. One well-known example of such a repository is the huge dataset of street-level pictures collected by Google Street View [23], through image-acquiring devices mounted onboard its dedicated cars, bikes, and other transportation means. Its coverage is extensive, and in many cases each location has been captured multiple times over the years.…”

Section: Data Retrievalmentioning

confidence: 99%

Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination

Iannelli¹,

Dell’Acqua

2017

Urban Science

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Abstract:In order for a risk assessment to deliver sensible results, exposure in the concerned area must be known or at least estimated in a reliable manner. Exposure estimation, though, may be tricky, especially in urban areas, where large-scale surveying is generally expensive and impractical; yet, it is in urban areas that most assets are at stake when a disaster strikes. Authoritative sources such as cadastral data and business records may not be readily accessible to private stakeholders such as insurance companies; airborne and especially satellite-based Earth-Observation data obviously cannot retrieve all relevant pieces of information. Recently, a growing interest is recorded in the exploitation of street-level pictures, procured either through crowdsourcing or through specialized services like Google Street View. Pictures of building facades convey a great amount of information, but their interpretation is complex. Recently, however, smarter image analysis methods based on deep learning started appearing in literature, made possible by the increasing availability of computational power. In this paper, we leverage such methods to design a system for large-scale, systematic scanning of street-level pictures intended to map floor numbers in urban buildings. Although quite simple, this piece of information is a relevant exposure proxy in risk assessment. In the proposed system, a series of georeferenced images are automatically retrieved from the repository where they sit. A tailored deep learning net is first trained on sample images tagged through visual interpretation, and then systematically applied to the entire retrieved dataset. A specific algorithm allows attaching "number of floors" tags to the correct building in a dedicated GIS (Geographic Information System) layer, which is finally output by the system as an "exposure proxy" layer.

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“…Mičušík & Košecká (2009) and Zamir & Shah (2011), on the other hand, employ rectilinear (cubic) projections and SURF or SIFT operators for street panoramas. Majdik et al (2013) generate artificial affine views of the scene in order to overcome the large viewpoint differences between GSV and low altitude images. Others (Torii et al, 2009;Ventura & Höllerer, 2013) match directly on the spherical GSV panoramas but using much denser images than those freely available by Google.…”

Section: Introductionmentioning

confidence: 99%

Automatic Adjustment of Wide-Base Google Street View Panoramas

Boussias-Alexakis¹,

Tsironisa²,

Petsa³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper focuses on the issue of sparse matching in cases of extremely wide-base panoramic images such as those acquired by Google Street View in narrow urban streets. In order to effectively use affine point operators for bundle adjustment, panoramas must be suitably rectified to simulate affinity. To this end, a custom piecewise planar projection (triangular prism projection) is applied. On the assumption that the image baselines run parallel to the street façades, the estimated locations of the vanishing lines of the façade plane allow effectively removing projectivity and applying the ASIFT point operator on panorama pairs. Results from comparisons with multi-panorama adjustment, based on manually measured image points, and ground truth indicate that such an approach, if further elaborated, may well provide a realistic answer to the matching problem in the case of demanding panorama configurations.

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MAV urban localization from Google street view data

Cited by 82 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

Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination

Automatic Adjustment of Wide-Base Google Street View Panoramas

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