Abstract
In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.
Highlights
The break lines, known as characteristic lines, structure lines, or skeleton lines, play an important role in the generation of digital terrain models (DTMs) [1,2]
To evaluate the proposed method’s accuracy and performance, the mobile LiDAR systems (MLSs) point cloud, presented in FigTuoreev1a5luaaatnedthreepprreospeonsteindgmaebtohuotd2’s5a0cmcuroafcay rraoonaaddd,pwewrafaossrumussaeenddc..eT, htheerMoaLdSwpaosinctucrlbo-uffllda,npkreedsefnrotemd ibnotFhigsuidrees1. 5TTareaensd, proelpesre, sdernatiinnaggea,baonudt s2m50alml aroefaas orof audn,dweragsrouwsetdh. aTlohnegrothaedcwurabs lcinuerb, m-flaadnekethdefarorema bchoathllesnidgeins.gTfroeresc,uprbolberse, adkraliinnaegs ee,xatrnadctsimona.ll areas of undergrowth along the curb line, made the area challenging for curb break lines extraction
The proposed method was shown to be able to extract the curb break lines even in cloud-occluded areas caused by a parked cars and undergrowth
Summary
The break lines, known as characteristic lines, structure lines, or skeleton lines, play an important role in the generation of digital terrain models (DTMs) [1,2]. Brugelmann [6] shortly describes some methods for break lines extraction from airborne point clouds, testing an approach proposed by Forstner [7]. The elimination of the influence of off-terrain points within this estimation process works in a fully automated way and adapts itself to the data This modelling starts from a 2D approximation of the break line, which is iteratively refined. A different scan angle, a very high point density, and the complexity of the environment surrounding the road caused a low efficiency when the techniques for break line extraction used for ALSs were applied in mobile LiDAR systems (MLSs) collected data. TheTpheropprospeodsemd emthetohdodisispspeceicfiifcicfoforrcclloouudd poiinnttssggaatthheerreedduusisnigngmmoboibleilLeiDLiADRAsRysstyemstesm(MsL(MSs)LSs) andainsdcoismcopmospeodseodf ofifvfeivceocnosnesceuctuitviveestseteppss,,wwhhiicchh are prreesseenntteeddininFFigiguurere1.1E. aEcahcshtespteips eixspelxapinleadiniend in detadiel tianiltihnethfoellfowlloiwnginsgusbu-bse-scetciotinosn.s
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