Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data

Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.Comment: 6 pages, 5 figure

Automated segmentation, detection and fitting of piping elements from terrestrial LIDAR data

Since the invention of light detection and ranging (LIDAR) in the early 1960s, it has been adopted for use in numerous applications, from topographical mapping with airborne LIDAR platforms to surveying of urban sites with terrestrial LIDAR systems. Static terrestrial LIDAR has become an especially effective tool for surveying, in some cases replacing traditional techniques such as electronic total stations and GPS methods. Current state-of-the-art LIDAR scanners have very fine spatial resolution, generating precise 3D point cloud data with millimeter accuracy. Therefore, LIDAR data can provide 3D details of a scene with an unprecedented level of details. However, automated exploitation of LIDAR data is challenging, due to the non-uniform spatial sampling of the point clouds as well as to the massive volumes of data, which may range from a few million points to hundreds of millions of points depending on the size and complexity of the scene being scanned. ^ This dissertation focuses on addressing these challenges to automatically exploit large LIDAR point clouds of piping systems in industrial sites, such as chemical plants, oil refineries, and steel mills. A complete processing chain is proposed in this work, using raw LIDAR point clouds as input and generating cylinder parameter estimates for pipe segments as the output, which could then be used to produce computer aided design (CAD) models of pipes. The processing chain consists of three stages: (1) segmentation of LIDAR point clouds, (2) detection and identification of piping elements, and (3) cylinder fitting and parameter estimation. The final output of the cylinder fitting stage gives the estimated orientation, position, and radius of each detected pipe element. ^ A robust octree-based split and merge segmentation algorithm is proposed in this dissertation that can efficiently process LIDAR data. Following octree decomposition of the point cloud, graph theory analysis is used during the splitting process to separate points within each octant into components based on spatial connectivity. A series of connectivity criteria (proximity, orientation, and curvature) are developed for the merging process, which exploits contextual information to effectively merge cylindrical segments into complete pipes and planar segments into complete walls. Furthermore, by conducting surface fitting of segments and analyzing their principal curvatures, the proposed segmentation approach is capable of detecting and identifying the piping segments. ^ A novel cylinder fitting technique is proposed to accurately estimate the cylinder parameters for each detected piping segment from the terrestrial LIDAR point cloud. Specifically, the orientation, radius, and position of each piping element must be robustly estimated in the presence of noise. An original formulation has been developed to estimate the cylinder axis orientation using gradient descent optimization of an angular distance cost function. The cost function is based on the concept that surface normals of points in a cylinder point cloud are perpendicular to the cylinder axis. The key contribution of this algorithm is its capability to accurately estimate the cylinder orientation in the presence of noise without requiring a good initial starting point. After estimation of the cylinder\u27s axis orientation, the radius and position are then estimated in the 2D space formed from the projection of the 3D cylinder point cloud onto the plane perpendicular to the cylinder\u27s axis. With these high quality approximations, a least squares estimation in 3D is made for the final cylinder parameters. ^ Following cylinder fitting, the estimated parameters of each detected piping segment are used to generate a CAD model of the piping system. The algorithms and techniques in this dissertation form a complete processing chain that can automatically exploit large LIDAR point cloud of piping systems and generate CAD models

ROBUST TECHNIQUES FOR BUILDING FOOTPRINT EXTRACTION IN AERIAL LASER SCANNING 3D POINT CLOUDS

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD

Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Currently, much of the manual labor needed to generate as-built Building Information Models (BIMs) of existing facilities is spent converting raw Point Cloud Datasets (PCDs) to BIMs descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this paper presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via Singular Value Decomposition (SVD) and Maximum Likelihood Estimation Sample Consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. In order to evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.This is the accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/mice.12063/abstract