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The Americans with Disabilities Act (ADA) requires that the design, construction, and maintenance of curb ramps provide accessibility to persons with disabilities. Light detection and ranging (lidar) can efficiently collect high-accuracy and high-resolution three-dimensional (3D) geometric data, which can be leveraged for ADA compliance assessment in a virtual environment to help guide and improve field practice. Most existing studies do not consider many aspects of the field inspection process including local variations in slope measurements and the physical instrument characteristics, warranting a more detailed accuracy evaluation. Accordingly, a workflow for virtual ADA compliance inspection of curb ramps using lidar data is proposed and was applied to a controlled experiment using full-size ramp specimens to optimize parameters and compare methods with rigorous accuracy assessments. The proposed workflow consists of data preprocessing, digital elevation model generation, and digital inclinometer simulation. In the digital inclinometer simulation, we developed and implemented four different approaches including surface normal, immediate neighbor linear regression, scaled neighbor linear regression, and touching point extraction. Among these approaches, the novel touching point extraction method considers both the roughness of the surface and the scale in which a digital inclinometer measures the slope. The accuracy and effectiveness of the framework were demonstrated, resulting in a root-mean square error of 0.18% for the mean slope, which is on par with the accuracy of a digital inclinometer (0.2%, 1−σ). An example virtual experiment is provided using the proposed approach to identify the optimal spacing of measurement samples to determine the slope of a curb ramp for ADA compliance assessment. [ABSTRACT FROM AUTHOR]

Road markings are essential features to convey important information to various roadway users such as pedestrians, bicyclists, and motorists. Although mobile laser scanning (MLS) technology provides dense and spatially accurate data, automated identification of road markings remains a challenge. Although approaches have been developed to detect road markings from point clouds, prior studies have not thoroughly investigated road discretization and road marking clustering, which are essential for effective instance-level identification and management of road markings. To improve road discretization, our approach uses line segmentation to divide the trajectory of MLS data and fit circles to individual segments. By calculating the intersection between the road points and the circle's center for each trajectory segment, this method ensures no overlap occurs when discretizing roads with sharp turns. To improve road marking clustering, the discretized points are rasterized onto a two-dimensional (2D) image and clustered using connected component labeling. Individual markings are skeletonized to detect junctions and corners, which enables the separation of underclustered road markings. Overclustered road markings are then merged using a rule-based approach optimized with a traffic line manual. The proposed approach was evaluated through extensive experiments using 28 MLS data sets containing 2,340 road marking clusters with complex geometry and wear. Out of these, only 64 instances (2.7%) were falsely clustered, achieving a precision rate of 97.5% and a recall rate of 99.7%. Practical Applications: Efficient road maintenance and improved road safety rely heavily on the clear visibility of road markings, which guide diverse users, such as pedestrians, cyclists, and drivers. Traditional methods for inspecting these markings are often time-consuming and prone to human error. In this study, we use MLS technology to collect comprehensive and precise data on roadways. We have developed an automated technique that significantly enhances the identification and management of road markings. This method accurately rasterizes and segments roadway lidar data—even in areas with sharp turns—to apply advanced image processing for the recognition of individual markings, including those that are substantially worn or have complex geometries. This simplifies the geometric data in a GIS database such that each line object is more representative of how someone would manually digitize the marking. Tested on numerous data sets, our approach achieved an accuracy rate of over 97% in clustering individual road markings. The developed approach not only streamlines roadway maintenance but also offers potential benefits for intelligent transportation systems. It contributes to safer autonomous vehicle navigation and supports informed decision-making regarding road infrastructure, ultimately improving all road users' experience. [ABSTRACT FROM AUTHOR]

Dense three-dimensional (3D) point clouds collected from rapidly evolving data acquisition techniques such as light detection and ranging (lidar) and structure from motion (SfM) multiview stereo (MVS) photogrammetry contain detailed geometric information of a scene suitable for a wide variety of applications. Among the many processes within a typical point cloud processing workflow, segmentation is often a crucial step to group points with similar attributes to support more advanced modeling and analysis. Segmenting large point cloud data sets (i.e., hundreds of millions to billions of points) can be extremely time consuming and tedious with current tools, which primarily rely on significant manual effort. While many automated methods have been proposed, the practicality, scalability, and versatility of these approaches remain a bottleneck stifling processing of large data sets. To overcome these challenges, this paper introduces a novel, generalized segmentation framework called Vo-Norvana, which incorporates a new voxelization technique, a normal variation analysis considering the positioning uncertainty of the point cloud, and a custom region growing process for clustering. The proposed framework was tested with several large-volume data sets collected in diverse scene types using several data acquisition platforms including terrestrial lidar, mobile lidar, airborne lidar, and drone-based SfM-MVS photogrammetry. In evaluating the accuracy of models generated from Vo-Norvana against manual segmentation, the average error of the position, orientation, and dimensions are 2.7 mm, 0.083°, and 0.9 mm, respectively. Over 0.2 million points per second and 36 thousand voxels per second can be achieved when segmenting an airborne lidar data set containing over 639 million points to about 1 million segments. [ABSTRACT FROM AUTHOR]

Terrestrial Laser Scanning (TLS) utilizes light detection and ranging (lidar) to effectively and efficiently acquire point cloud data for a wide variety of applications. Segmentation is a common procedure of post-processing to group the point cloud into a number of clusters to simplify the data for the sequential modelling and analysis needed for most applications. This paper presents a novel method to rapidly segment TLS data based on edge detection and region growing. First, by computing the projected incidence angles and performing the normal variation analysis, the silhouette edges and intersection edges are separated from the smooth surfaces. Then a modified region growing algorithm groups the points lying on the same smooth surface. The proposed method efficiently exploits the gridded scan pattern utilized during acquisition of TLS data from most sensors and takes advantage of parallel programming to process approximately 1 million points per second. Moreover, the proposed segmentation does not require estimation of the normal at each point, which limits the errors in normal estimation propagating to segmentation. Both an indoor and outdoor scene are used for an experiment to demonstrate and discuss the effectiveness and robustness of the proposed segmentation method. [ABSTRACT FROM AUTHOR]

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation. [ABSTRACT FROM AUTHOR]

Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitudefrequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling. [ABSTRACT FROM AUTHOR]

Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the point clouds, processing MLS data can be still challenging with respect to effectiveness, efficiency, and versatility. This paper proposes an efficient MLS data processing framework for general purposes consisting of three main steps: trajectory reconstruction, scan pattern grid generation, and Mo-norvana (Mobile Normal Variation Analysis) segmentation. We present a novel approach to reconstructing the scanner trajectory, which can then be used to structure the point cloud data into a scan pattern grid. By exploiting the scan pattern grid, point cloud segmentation can be performed using Mo-norvana, which is developed based on our previous work for processing Terrestrial Laser Scanning (TLS) data, normal variation analysis (Norvana). In this work, with an unorganized MLS point cloud as input, the proposed framework can complete various tasks that may be desired in many applications including trajectory reconstruction, data structuring, data visualization, edge detection, feature extraction, normal estimation, and segmentation. The performance of the proposed procedures are experimentally evaluated both qualitatively and quantitatively using multiple MLS datasets via the results of trajectory reconstruction, visualization, and segmentation. The efficiency of the proposed method is demonstrated to be able to handle a large dataset stably with a fast computation speed (about 1 million pts/sec. with 8 threads) by taking advantage of parallel programming. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications. [ABSTRACT FROM AUTHOR]

For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets. [ABSTRACT FROM AUTHOR]

Abstract Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro -reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types. [ABSTRACT FROM AUTHOR]