Pingbo Tang

Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces

In many construction and infrastructure management projects, it is important to ensure the flatness of concrete surfaces. Inspectors assess the quality of flat surface construction by checking whether a surface deviates from perfectly flat by more than a specified tolerance. Current flatness assessment methods, such as using a straightedge or shape profiler, are limited in the speed or density of their measurements. Laser scanners are general-purpose instruments for densely and accurately measuring three-dimensional shapes. In this paper, we show how laser scanners can be effectively used to assess surface flatness. Specifically, we formalize, implement, and validate three algorithms for processing laser-scanned data to detect surface flatness deviations. Since different scanners and algorithms can perform differently, we define an evaluation framework for objectively evaluating the performance of different algorithms and scanners. Using this framework, we analyze and compare the performance of the three algorithms using data from three laser scanners. The results show that it is possible to detect surface flatness defects as small as 3 cm across and 1 mm thick from a distance of 20 m.

A Comparative Analysis of Depth-Discontinuity and Mixed-Pixel Detection Algorithms

Laser scanner measurements are corrupted by noise and artifacts that can undermine the performance of registration, segmentation, surface reconstruction, recognition, and other algorithms operating on the data. While much research has addressed laser scanner noise models, comparatively little is known about other artifacts, such as the mixed pixel effect, color-dependent range biases, and specular reflection effects. This paper focuses on the mixed pixel effect and the related challenge of detecting depth discontinuities in 3D data. While a number of algorithms have been proposed for detecting mixed pixels and depth discontinuities, there is no consensus on how well such algorithms perform or which algorithm performs best. This paper presents a comparative analysis of five mixed-pixel/discontinuity detection algorithms on real data sets. We find that an algorithm based on the surface normal angle has the best overall performance, but that no algorithm performs exceptionally well. Factors influencing algorithm performance are also discussed.

Using laser scanners for modeling and analysis in architecture, engineering, and construction

Laser scanners are rapidly gaining acceptance as a tool for three dimensional (3D) modeling and analysis in the architecture, engineering, and construction (AEC) domain. Since 2001, our cross-disciplinary research team has been developing new methods for analyzing and modeling laser scanner data, with an emphasis on applications in the AEC domain. This paper provides an overview of our groups recent research efforts. Our work includes improving our understanding of the low-level aspects of laser scanner data, using comparison methods to analyze laser scanner data and derived models, and developing modeling and recognition algorithms to support the automatic creation of building models from laser scan data.

Sensing and Field Data Capture for Construction and Facility Operations

Collection of accurate, complete, and reliable field data is not only essential for active management of construction projects involving various tasks, such as material tracking, progress monitoring, and quality assurance, but also for facility and infrastructure management during the service lives of facilities and infrastructure systems. Limitations of current manual data collection approaches in terms of speed, completeness, and accuracy render these approaches ineffective for decision support in highly dynamic environments, such as construction and facility operations. Hence, a need exists to leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations. These technologies can be used not only for acquiring data about the various operations being carried out at construction and facility sites but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these o...

Efficient and Effective Quality Assessment of As-Is Building Information Models and 3D Laser-Scanned Data

Documenting as-is conditions of buildings using 3D laser scanning and Building Information Modeling (BIM) technology is being adopted as a practice for enhancing effective management of facilities. Many service providers generate as-is BIMs based on laser-scanned data. It is necessary to conduct timely and comprehensive assessments of the quality of the laser-scanned data and the as-is BIM generated from the data before using them for making decisions about facilities. This paper presents the data and as-is BIM QA requirements of civil engineers and demonstrates that the required QA information can be derived by analyzing the patterns in the deviations between the data and the as-is BIMs. We formalized this idea as a deviation analysis method for efficient and effective QA of the data and as-is BIMs. A preliminary evaluation of the results obtained through this approach show the potential of this method for achieving timely, detailed, comprehensive, and quantitative assessment of various types of data/model quality issues.

Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals

With the capability of capturing detailed geometry of bridges in minutes, laser scanning technology has attracted the interests of bridge inspectors and researchers in the domain of bridge management. A challenge of effectively utilizing laser scanned point clouds for bridge inspection is that inspectors need to manually extract and measure large numbers of geometric features (e.g., points) for deriving geometric information items (e.g., the minimum underclearance) of bridges, named as bridge surveying goals in this research. Tedious manual data processing impedes inspectors from quantitatively understanding how various data processing options (e.g., algorithms, parameter values) influence the data processing time and the reliabilities of the surveying goal results. This paper shows the needs of automatic workflow executions for extracting surveying goals from laser scanned point clouds, and presents a computational framework for addressing these needs. This computational framework is composed of formal representations of workflows and mechanisms for constructing and executing workflows. Using a prototype system implemented based on this framework, we constructed and quantitatively characterized three workflows for extracting three representative bridge surveying goals, using three metrics of workflow performance defined in this research: exhaustiveness of measurement sampling, reliability of surveying goal results, and time efficiency.

Target-Focused Local Workspace Modeling for Construction Automation Applications

AbstractsTo improve the efficiency and effectiveness of the geometric data collection and processing for automated construction operations, a light-weight hybrid light amplification for detection and ranging (LADAR) system was developed to enable semi-automatic targeted data collection, such that an operator can quickly obtain high-quality data for parts of the workspace critical for an on-going operation. To further improve the precision of separating needed data from background and irrelevant data, using this custom-designed system, the relationships were investigated among the amplitude of data points, the scanning distance, time-of-flight, and the grayscales of objects. Thus, positive control of scanning position, orientation, field of view and amplitude is enabled. For construction robotics applications in confined spaces such as power plants, such positive control is a critical capability. The evaluation results indicate the efficiency and effectiveness of the semi-automatic targeted scanning approa...

Characterization of three algorithms for detecting surface flatness defects from dense point clouds

Surface flatness assessment is required for controlling the quality of various products, such as building and mechanical components. During such assessments, inspectors collect data capturing surface shape, and use it to identify flatness defects, which are surface parts deviating from a reference plane by more than the tolerance. Laser scanners can deliver accurate and dense 3D point clouds capturing detailed surface shape for flatness defect detection in minutes. However, few studies explore algorithms for detecting surface flatness defects from dense point clouds, and provide quantitative analysis of defect detection performance. This paper presents three surface-flatness-defect detection algorithms and our experimental investigations for characterizing their performances. We created a test bed, which is composed of several flat boards with defects of various sizes on them, and tested two scanners and three algorithms using it. The results are reported in the form of a set of performance maps indicating under which conditions (using which scanner, scanning distance, selected defect detection algorithm, and angular resolution of the scanner, etc.), what types of defects are detected. Our analysis shows that scanning distance and angular resolution substantially influence the detection accuracy. Comparative analyses of scanners and defect detection algorithms are also presented.

Semi-Automated As-Built Modeling of Light Rail System Guide Beams

For a light rail system, smooth contact between the vehicle and the guide beam is critical for reducing the friction and the vibration of an operating vehicle. Therefore, the shape of guide beams needs to be controlled with mm-level accuracy during the construction. Currently, most methods for detecting shape defects of guide beams, such as experimental run of vehicles, are costly and tedious, and can only identify defects after the completion of the construction, causing reworks and delays. From dense point clouds collected by laser scanners, inspectors can manually extract geometric features and conduct virtual inspections of guide beams. However, the manual geometric feature extraction process impedes effective utilization of point clouds for the shape analysis of guide beams. This research developed a semi-automatic approach for simultaneously extracting the axis parameters (e.g., radius) and cross-section features (e.g., width) of a guide beam using a Hough-Transform based approach, and discusses factors (e.g., data density) influencing the performance of this approach.

Laser Scanning for Bridge Inspection and Management

The current process for acquiring bridge geometric data for the National Bridge Inventory (NBI) is based on manual surveying, manual data processing and interpretation. Hence, it is time-consuming and error-prone. This paper presents a laser-scanning-based approach to acquire geometric data for bridge inspection, describes a case study and discusses the advantages of this approach over current practice from the perspectives of both bridge inspection and management. Both current approach and laser-scanning based approach are composed of three major steps of data collection, data processing and data interpretation. Yet, a comparison of these approaches highlights major differences in the accuracy and comprehensiveness of the data collected. Based on the comparison, the authors suggest a need for a formalized way to decompose higher level bridge inspection goals to enable successful application of laser scanning technology for bridge inspection.