Automatic Classification of Point Clouds for Highway Documentation

Hůlková, Martina; Pavelka, Karel; Matoušková, Eva

doi:10.14311/ap.2018.58.0165

Cited by 13 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of principal component analysis for the assessment of bridge's structural health is described in [28]. The feature extraction approach with a statistical classification and machine learning methods for the transportation infrastructure was proposed in [29]. The mechanical simulation is used for an unsupervised feature detection and data fusion in [30], for rail-infrastructure monitoring that is based on operational trains.…”

Section: No /mentioning

confidence: 99%

Indicators for Common Crossing Structural Health Monitoring With Track-Side Inertial Measurements

et al. 2019

View full text Add to dashboard Cite

This paper focuses on the experimental study of an alteration in the railway crossing dynamic response due to the rolling surface degradation during a crossing’s lifecycle. The maximal acceleration measured with the track-side measurement system as well as the impact position monitoring show no significant statistical relation to the rolling surface degradation. The additional spectral features are extracted from the acceleration measurements with a wavelet transform to improve the information usage. The reliable prediction of the railway crossing remaining useful life (RUL) demands the trustworthy indicators of structural health that systematically change during the lifecycle. The popular simple machine learning methods like principal component analysis and partial least square regression are used to retrieve two indicators from the experimental information. The feature ranking and selection are used to remove the redundant information and increase the relation of indicators to the lifetime.

show abstract

Section: No /mentioning

confidence: 99%

Indicators for Common Crossing Structural Health Monitoring With Track-Side Inertial Measurements

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Chen et al [40] selected the peaks of intensity values as candidate lane-marking points for each scan line through an adaptive thresholding process. Moreover, 2D projective feature images generated from 3D points are commonly used to extract road markings [41][42][43]45,46]. Kumar et al [41] introduced a range-dependent thresholding method to extract road markings from 2D intensity and range images.…”

Section: Introductionmentioning

confidence: 99%

“…Soilán et al [43] selected cells with similar angles in the intensity image as a mask and applied the Otsu thresholding process over each mask. Ishikawa et al [45] and Hůlková et al [46] generated 2D images from 3D points to accelerate the calculation. Yu et al [44] developed a trajectory-based multisegment thresholding method to directly extract road markings from the point cloud.…”

Section: Introductionmentioning

confidence: 99%

Combined Lane Mapping Using a Mobile Mapping System

et al. 2019

View full text Add to dashboard Cite

High-definition mapping of 3D lane lines has been widely needed for the highway documentation and intelligent navigation of autonomous systems. A mobile mapping system (MMS) captures both accurate 3D LiDAR point clouds and high-resolution images of lane markings at highway driving speeds, providing an abundant data source for combined lane mapping. This paper aims to map lanes with an MMS. The main contributions of this paper include the following: (1) an intensity correction method was introduced to eliminate the reflectivity inconsistency of road-surface LiDAR points; (2) a self-adaptive thresholding method was developed to extract lane markings from their complicated surroundings; and (3) a LiDAR-guided textural saliency analysis of MMS images was proposed to improve the robustness of lane mapping. The proposed method was tested with a dataset acquired in Wuhan, Hubei, China, which contained straight roads, curved roads, and a roundabout with various pavement markings and a complex roadside environment. The experimental results achieved a recall of 96.4%, a precision of 97.6%, and an F-score of 97.0%, demonstrating that the proposed method has strong mapping ability for various urban roads.

show abstract

“…Experts from a wide range of disciplines use complex spatial data to solve specialized tasks, such as creating noise maps, highway inventory [1], modelling air pollution, estimating the solar potential of roof structures, planning new wireless infrastructures, designing houses by taking natural daylight requirements into consideration and generating virtual environments for flight simulators. These tasks require the use of digital elevation models and 3D building models with generalized roof structures also referred to as Level of Detail 2 (LoD2) buildings [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Reconstruction of Roof Models From Building Outlines and Aerial Image Data

Hron

Halounová

2019

Acta Polytech

View full text Add to dashboard Cite

The knowledge of roof shapes is essential for the creation of 3D building models. Many experts and researchers use 3D building models for specialized tasks, such as creating noise maps, estimating the solar potential of roof structures, and planning new wireless infrastructures. Our aim is to introduce a technique for automating the creation of topologically correct roof building models using outlines and aerial image data. In this study, we used building footprints and vertical aerial survey photographs. Aerial survey photographs enabled us to produce an orthophoto and a digital surface model of the analysed area. The developed technique made it possible to detect roof edges from the orthophoto and to categorize the edges using spatial relationships and height information derived from the digital surface model. This method allows buildings with complicated shapes to be decomposed into simple parts that can be processed separately. In our study, a roof type and model were determined for each building part and tested with multiple datasets with different levels of quality. Excellent results were achieved for simple and medium complex roofs. Results for very complex roofs were unsatisfactory. For such structures, we propose using multitemporal images because these can lead to significant improvements and a better roof edge detection. The method used in this study was shared with the Czech national mapping agency and could be used for the creation of new 3D modelling products in the near future.

show abstract

Automatic Classification of Point Clouds for Highway Documentation

Cited by 13 publications

References 5 publications

Indicators for Common Crossing Structural Health Monitoring With Track-Side Inertial Measurements

Indicators for Common Crossing Structural Health Monitoring With Track-Side Inertial Measurements

Combined Lane Mapping Using a Mobile Mapping System

Automatic Reconstruction of Roof Models From Building Outlines and Aerial Image Data

Contact Info

Product

Resources

About