An evaluation framework for benchmarking indoor modelling methods

Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference.Xunta de Galicia | Ref. ED481B 2016/079-

AN EVALUATION FRAMEWORK FOR BENCHMARKING INDOOR MODELLING METHODS

Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference

Terrestrial LiDAR-based bridge evaluation

Considering the over half million bridges in the US state highway system, more than 70% of which were built before 1935, it is of little wonder that bridge maintenance and management is facing severe challenges and the significant funding scarcity rapidly escalates the problem. Commercial remote sensing techniques have the capability of covering large areas and are suggested to be cost effective methods for bridge inspection. This dissertation introduces several applications of the remote bridge inspection technologies using ground-based LiDAR systems. In particular, the application of terrestrial LiDAR for bridge health monitoring is studied. An automatic bridge condition evaluation system based on terrestrial LiDAR data, LiBE (LiDAR-based Bridge Evaluation), is developed. The research works completed thus far have shown that LiDAR technology has the capability for bridge surface defect detection and quantification, clearance measurement, and displacement measurement during bridge static load testing. Several bridges in Mecklenburg County, NC, and other areas have been evaluated using LiBE and quantitative bridge rating mechanisms are proposed. A cost-benefit analysis has been conducted that demonstrates the relevancy of the technique to current nation-wide bridge management problem, as well as, the potential of reducing the bridge maintenance costs to the stack holders. The results generated from these technologies are valuable for bridge maintenance decision making

Automated Extraction of Road Information from Mobile Laser Scanning Data

Effective planning and management of transportation infrastructure requires adequate geospatial data. Existing geospatial data acquisition techniques based on conventional route surveys are very time consuming, labor intensive, and costly. Mobile laser scanning (MLS) technology enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced point cloud data in the format of three-dimensional (3D) point clouds. Today, more and more commercial MLS systems are available for transportation applications. However, many transportation engineers have neither interest in the 3D point cloud data nor know how to transform such data into their computer-aided model (CAD) formatted geometric road information. Therefore, automated methods and software tools for rapid and accurate extraction of 2D/3D road information from the MLS data are urgently needed. This doctoral dissertation deals with the development and implementation aspects of a novel strategy for the automated extraction of road information from the MLS data. The main features of this strategy include: (1) the extraction of road surfaces from large volumes of MLS point clouds, (2) the generation of 2D geo-referenced feature (GRF) images from the road-surface data, (3) the exploration of point density and intensity of MLS data for road-marking extraction, and (4) the extension of tensor voting (TV) for curvilinear pavement crack extraction. In accordance with this strategy, a RoadModeler prototype with three computerized algorithms was developed. They are: (1) road-surface extraction, (2) road-marking extraction, and (3) pavement-crack extraction. Four main contributions of this development can be summarized as follows. Firstly, a curb-based approach to road surface extraction with assistance of the vehicle’s trajectory is proposed and implemented. The vehicle’s trajectory and the function of curbs that separate road surfaces from sidewalks are used to efficiently separate road-surface points from large volume of MLS data. The accuracy of extracted road surfaces is validated with manually selected reference points. Secondly, the extracted road enables accurate detection of road markings and cracks for transportation-related applications in road traffic safety. To further improve computational efficiency, the extracted 3D road data are converted into 2D image data, termed as a GRF image. The GRF image of the extracted road enables an automated road-marking extraction algorithm and an automated crack detection algorithm, respectively. Thirdly, the automated road-marking extraction algorithm applies a point-density-dependent, multi-thresholding segmentation to the GRF image to overcome unevenly distributed intensity caused by the scanning range, the incidence angle, and the surface characteristics of an illuminated object. The morphological operation is then implemented to deal with the presence of noise and incompleteness of the extracted road markings. Fourthly, the automated crack extraction algorithm applies an iterative tensor voting (ITV) algorithm to the GRF image for crack enhancement. The tensor voting, a perceptual organization method that is capable of extracting curvilinear structures from the noisy and corrupted background, is explored and extended into the field of crack detection. The successful development of three algorithms suggests that the RoadModeler strategy offers a solution to the automated extraction of road information from the MLS data. Recommendations are given for future research and development to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

Mobile laser scanning based determination of railway network topology and branching direction on turnouts

GNSS is often inaccurate and satellite signals are not always available, which results in ambiguous situations. In order to reduce their negative effects on train-borne localization, this work proposes an approach for the detection of tracks, turnouts, and branching directions solely from 2d lidar sensor measurements. The experimental evaluation shows highly correct and complete results. In summary, these detections are sufficient to reduce ambiguity problems in train-borne localization

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways