Digitizing and 3D modeling of urban envirom-nents and roads using vehicle-borne laser scanner system

Abstract

Absrroer-In this paper we present a system far threedimensional environment modeling. It consists of an instrumented vehicle equipped with B 2D laser range scanner for data mapping, and GPS, LYS and odometers for vehicle positioning and anitude information. The advantage of this system is its ahilie to perform data acquisition during the vehicle navigation; the sensor needed being a basic 2D scanner with opposition to traditional expensive 3D SEOSOTS. This system integrates the laser raw range data with the vehicle's internal state estimator and is capable of rceonstructing the 3D geometry of the environment by realtime geo-referencing.We propose a high level representation of the urban scene while identifying automstiesiiy and in real time some types of existing objects in this environment. Thus, our modeling is articulated around three principal axes, the segmentation; decimation; the 3D reconstruction and visualization. The road is the most important object lor us; some road features like the curvature and the width are extracted.

3D reconstruction of road surfaces using an integrated multi-sensory approach

“…Their system outputs binary crack maps along with longitudinal road parameters. Abuhadrous [16] also scans road surfaces together with various spatial objects like traffic signs, trees and vegetation in the field of view. The processing methods in his system extract range points belonging to road edges and centers, and then model the road surfaces using feature triangulation.…”

Use of mobile LiDAR in road information inventory: a review

“…Several studies first segmented 3D points into line sections, on which laser data features and road properties were analysed to obtain road information (Abuhadrous et al 2004, McElhinney et al 2010, Cabo et al 2015, Riveiro et al 2015. Abuhadrous et al (2004) classified points into buildings, roads and trees by analysing two histograms in the Z-and Y-directions in one profile to reflect the horizontal or vertical nature of urban targets.…”

“…Abuhadrous et al (2004) classified points into buildings, roads and trees by analysing two histograms in the Z-and Y-directions in one profile to reflect the horizontal or vertical nature of urban targets.…”

Three-level frame and RD-schematic algorithm for automatic detection of individual trees from MLS point clouds

“…All in all, the conventional processing scenarios for ALS and TLS data are not suitable for MLS, and a novel scheme for automatic detection of individual trees from MLS point clouds is needed. This trend recently keeps being strengthened due to the shortage of tree detection methods hindering further research on physiochemical parameters, which can be learnt from the phenomenon of the developed methods for MLS mainly concentrating on regularshaped objects, for example urban buildings and roads (Fuerstenberg et al 2002, Zhao and Shibasaki 2003, Abuhadrous et al 2004, Lasky et al 2006, Anttoni et al 2008.…”