Accuracy verification of the Lynx Mobile Mapper system

Puente, Iván; González-Jorge, H.; Riveiro, Belén; Arias, Pedro

doi:10.1016/j.optlastec.2012.05.029

Cited by 72 publications

(45 citation statements)

References 6 publications

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“…Furthermore, it is equipped with four 5-MPix JAI cameras which are synced with the scanners (Fig 5a). An analysis of the MMS can be found in (Puente et al, 2013).…”

Section: Case Studymentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

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ABSTRACT:The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

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“…Furthermore, it is equipped with four 5-MPix JAI cameras which are synced with the scanners (Fig 5a). An analysis of the MMS can be found in (Puente et al, 2013).…”

Section: Case Studymentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

Self Cite

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“…The mobile system is composed of two LiDAR sensors, an illuminance meter, four digital cameras and a navigation system with global navigation and inertial measurement units (IMU) with a two-antenna heading measurement system, called global navigation satellite system (GNSS). A complete accuracy study and system review can be found in Puente et al [38]. …”

Section: Road Street Lighting Facilitymentioning

confidence: 99%

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

2018

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This document addresses the development of a novel methodology to identify the actual maintenance factor of the luminaires of an outdoor lighting installation in order to assess their lighting performance. The method is based on the combined use of Radiance, a free and open-source tool, for the modeling and simulation of lighting scenes, and GenOpt, a generic optimization program, for the calibration of the model. The application of this methodology allows the quantification of the deterioration of the road lighting system and the identification of luminaires that show irregularities in their operation. Values lower than 9% for the error confirm that this research can contribute to the management of street lighting by assessing real road conditions.

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“…The 46 control points where distributed along a 5 km route under representative Central European GNSS conditions. The use of a specifically designed calibration object is discussed in (Puente et al, 2012). The 3 m x 3 m test field consists of 5 spheres with 100 mm in diameter precisely calibrated.…”

Section: Artificial Test Fields As Referencementioning

confidence: 99%

“…Several examples of accuracy analysis and validation of Mobile Mapping Systems can be found in the literature, e.g. (Graefe et al, 2001), (Kremer and Hunter, 2007), (Haala et al, 2008), (Puente et al, 2012) and (Toschi et al, 2015), which will be discussed in the following paragraphs.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment of Mobile Mapping Point Clouds Using the Existing Environment as Terrestrial Reference

Hofmann

Brenner

2016

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

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ABSTRACT:Mobile mapping data is widely used in various applications, what makes it especially important for data users to get a statistically verified quality statement on the geometric accuracy of the acquired point clouds or its processed products. The accuracy of point clouds can be divided into an absolute and a relative quality, where the absolute quality describes the position of the point cloud in a world coordinate system such as WGS84 or UTM, whereas the relative accuracy describes the accuracy within the point cloud itself. Furthermore, the quality of processed products such as segmented features depends on the global accuracy of the point cloud but mainly on the quality of the processing steps. Several data sources with different characteristics and quality can be thought of as potential reference data, such as cadastral maps, orthophoto, artificial control objects or terrestrial surveys using a total station. In this work a test field in a selected residential area was acquired as reference data in a terrestrial survey using a total station. In order to reach high accuracy the stationing of the total station was based on a newly made geodetic network with a local accuracy of less than 3 mm. The global position of the network was determined using a long time GNSS survey reaching an accuracy of 8 mm. Based on this geodetic network a 3D test field with facades and street profiles was measured with a total station, each point with a two-dimensional position and altitude. In addition, the surface of poles of street lights, traffic signs and trees was acquired using the scanning mode of the total station. Comparing this reference data to the acquired mobile mapping point clouds of several measurement campaigns a detailed quality statement on the accuracy of the point cloud data is made. Additionally, the advantages and disadvantages of the described reference data source concerning availability, cost, accuracy and applicability are discussed.

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Accuracy verification of the Lynx Mobile Mapper system

Cited by 72 publications

References 6 publications

Automatic Road Sign Inventory Using Mobile Mapping Systems

Automatic Road Sign Inventory Using Mobile Mapping Systems

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

Accuracy Assessment of Mobile Mapping Point Clouds Using the Existing Environment as Terrestrial Reference

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