Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points, weighted according to its distance to the sensor, and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create and filter triangles for each triplet of self-connected edges and according to their local planarity. We compare our results to an unweighted simplicial complex reconstruction.Comment: 8 pages, 11 figures, CFPT 2018. arXiv admin note: substantial text overlap with arXiv:1802.0748

Urban accessibility diagnosis from mobile laser scanning data

International audienceIn this paper we present an approach for automatic analysis of urban acessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning. Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards. Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases

AUTOMATIC CLASSIFICATION OF POINT CLOUDS FOR HIGHWAY DOCUMENTATION

Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous datasets acquired by these systems is the increase in the demands on the fast and effective processing of these datasets. The crucial part of the roadway datasets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long dataset obtained by LYNX Mobile Mapper over a highway

Accuracy assessment of mobile laser scanning elevation data in different vegetation areas

Käesoleva bakalaureusetöö eesmärgiks on hinnata mobiilse laserskaneerimise kõrguslikku täpsust erineva taimestiku puhul ning uurida, kas see meetod võiks olla alternatiiviks aerolaserskaneerimisele. Käesolevas töös kasutatud andmed on saadud 2015. aasta suvel toimunud Tallinn-Tartu maantee, Põltsamaa-Kärevere lõigu mobiilse laserskaneerimise käigus. Uurimiseks valiti välja kolm erineva taimestikuga polügooni. Kõrgusliku täpsuse hindamiseks võrreldi mobiilse laserskaneerimise käigus saadud andmeid looduses GNSS seadme abil mõõdetud kontrollpunktide kõrgustega. Objektiivse hinnangu andmiseks võrreldi mobiilse laserskaneerimse kõrguslike andmete täpsust ka samade andmete põhjal joonistatud maapinna profiilide kõrguste täpsusega ja Maa-ameti aerolaserskaneerimise andmete kõrguste täpsusega. Antud töös saadi erinevate polügoonide mobiilse laserskaneerimise andmete keskmisteks ruutvigadeks järgmised suurused. Keskmise taimestikuga I polügoonil, milleks oli umbes meetri kõrguse taimkattega põld, 0,98 meetrit. Madala taimestikuga II polügoonil, milleks oli väga madala ja hõreda taimkattega karjamaa, 0,23 meetrit. Kõrge taimestikuga III polügoonil, kus asus võssa kasvanud kraav ning selle taga põld, 0,61 meetrit. Uurimistöö käigus selgus, et mobiilse laserskaneerimise kõrguslik täpsus sõltub olulisel määral mõõdetava piirkonna taimestiku tihedusest ning, et maapinna profiilide välja joonistamine tõstab lõpptulemuse täpsust. Töö tulemustest tuli välja ka see, et antud juhul olid aerolaserskanneerimise kõrguslikud andmed tunduvalt täpsemad (keskmine ruutviga 0,20 meetrit), kui mobiilse laserskaneerimise kõrguslikud andmed (keskmine ruutviga 0,70 meetrit). Selle põhjal järeldati, et mobiilset laserskaneerimist oleks kõige mõistlikum teostada aastaajal, mil taimestik on hõredam.The aim of current study was to assess the accuracy of mobile laser scanning elevation data in different vegetation areas and to explore if mobile laser scanning could be used as an alternative to aerial laser scanning. Data used in current study was collected in summer of 2015., during mobile laser scanning of Põltsamaa-Kärevere section of E263 route (Tallinn-Tartu-Võru-Luhamaa). Three smaller, differently vegetated, sections were picked from the large project to study the accuracy of elevation data. For accuracy assessment, the mobile laser scanning elevation data was compared to the checkpoints measured with GNSS device. Ground profiles were drawn based on mobile laser scanning data. For objective assessment, accuracy of mobile laser scanning elevation data was compared to accuracy of ground profile elevation data and aerial laser scanning elevation data. The study found that the RMSE in the I section, which was a field vegetated with 1 meter high crop, was 0,98 meters. RMSE in the II section, which was a pasture with low and sparse vegetation, was 0,23 meters. RMSE in the III section, which contained a bushy ditch and a field behind it, was 0,61 meters. Results show that the accuracy of mobile laser scanning elevation data depends substantially on the density of vegetation in scanned areas and that drawing ground profiles reduced the RMSE of mobile laser scanning elevation data. The results show that, in this case, aerial laser scanning elevation data was considerably more accurate (RMSE 0,20 m), then mobile laser scanning elevation data (RMSE 0,70 m). On this basis it can be concluded, that the most reasonable time to conduct mobile laser scanning would be during a season, when vegetation is the sparsest

Mobile laser scanning, for monitoring polythylene city infrastructure network.

This research discusses a more efficient geospatial monitoring technique for city infrastructure networks. It will concentrate on polyethylene city infrastructure materials, where power, water and communication networks are covered or protected by polyethylene materials. A technical comparison is conducted between current and proposed geospatial monitoring techniques in order to develop an overall performance evaluation. The mobile laser scanning technology achieved the best performance evaluation, where detailed data analysis and collection, mobile laser missions, modeling and interpretation, and system geometrical corrections for location and orientation have also been conducted. Prior to conducting the performance evaluation, the research investigates mobile laser behavior and recognition capabilities with respect to Polyethylene City infrastructure materials. After analyzing the mobile laser pulses behavior, and its correlations with the mission ground speed and exposed scanned surface, it is concluded that the mobile laser pulses response is constant for the Polyethylene City infrastructure materials. The concluded mobile laser pulses constant is utilized to develop a mathematical model for re-planning the mobile laser scanning missions to obtain the best model for monitoring the Polyethylene City infrastructure networks

Planar projection of mobile laser scanning data in tunnels

Laser scanning is now a common technology in the surveying and monitoring of large engineering infrastructures, such as tunnels, both in motorways and railways. Extended possibilities exist now with the mobile terrestrial laser scanning systems, which produce very large data sets that need efficient processing techniques in order to facilitate their exploitation and usability. This paper deals with the implementation of a methodology for processing and presenting 3D point clouds acquired by laser scanning in tunnels, making use of the approximately cylindrical shape of tunnels. There is a need for a 2D presentation of the 3D point clouds, in order to facilitate the inspection of important features as well as to easily obtain their spatial location. An algorithm was developed to treat automatically point clouds obtained in tunnels in order to produce rectified images that can be analysed. Tests were carried with data acquired with static and mobile Riegl laser scanning systems, by Artescan company, in highway tunnels in Portugal and Spain, with very satisfactory results. The final planar image is an alternative way of data presentation where image analysis tools can be used to analyze the laser intensity in order to detect problems in the tunnel structure