Automatic reconstruction of road surface features by using terrestrial mobile lidar

Guo, Jenhwa; Tsai, Meng‐Ju; Han, Jen-Yu

doi:10.1016/j.autcon.2015.07.017

Cited by 42 publications

(42 citation statements)

References 18 publications

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“…Based on semantic knowledge (e.g. shape and size) of road markings (Yang et al 2012b), the established image processing algorithms were applied, such as thresholding-based segmentation, Hough Transform, morphology and Multi-Scale Tensor Voting (MSTV) (Jaakkola et al 2008, Chen et al 2009, Vosselman 2009, Smadja et al 2010, Mancini et al 2012, Guo et al 2015, Riveiro et al 2015, Guan et al 2015b). The use of Hough transformation for road marking extraction is weakened by specifying the number of road markings to be detected, which is a limiting factor for complex types of road markings such as hatchings and words.…”

Section: Road Surface Structuresmentioning

confidence: 99%

“…Especially, in highly populated urban environments, high accident rates are caused by the absence of clearly presented road signals (Carnaby 2005). Road markings are highly retro-reflective surfaces painted on roads; reflectance of the target in the form of intensity can be used to identify road markings (Chen et al 2009, Mancini et al 2012, Guo et al 2015, Riveiro et al 2015.…”

Section: Road Surface Structuresmentioning

confidence: 99%

“…Road features include curb, minimal road width and elevation information. Guo et al (2015) combined road width with the elevation data of point clouds to filter out non-road surface points. Curbs are a critical feature for road extraction because curbs represent boundaries of roads in an urban environment.…”

Section: Road Pavement Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

174

119

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Mobile LiDAR technology is currently one of the attractive topics in the fields of remote sensing and laser scanning. Mobile LiDAR enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced data, in the form of three-dimensional (3D) point clouds. This technology has been gaining popularity in the recognition of roads and road-scene objects. A thorough review of available literature is conducted to inform the advancements in mobile LiDAR technologies and their applications in road information inventory. The literature review starts with a brief overview of mobile LiDAR technology, including system components, direct geo-referencing, data error analysis and geometrical accuracy validation. Then, this review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and polelike objects. Finally, the challenges and future trends are discussed. Our review demonstrates the great potential of mobile LiDAR technology in road information inventory. ARTICLE HISTORY

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Section: Road Surface Structuresmentioning

confidence: 99%

Section: Road Surface Structuresmentioning

confidence: 99%

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Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

174

119

View full text Add to dashboard Cite

show abstract

“…The three most common methods for detecting longrange obstacles are radar [5][6][7][8], laser scanner [9][10][11][12][13], and computer vision [14,15]. Each of them has advantages and disadvantages [16] and sensor fusion is commonly used to overcome the individual limitations [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

et al. 2016

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Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control. This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture, by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level. Considering that the guidance variables are more accurately estimated when dealing with reference points in front of and behind the vehicle, an algorithm based on vehicle dynamics mathematical model is proposed to extend the detected points in cases where the sensor is placed at the front of the vehicle. The algorithm has been tested when driving along a lane delimited by New Jersey barriers at both sides and the results show a correct behaviour. The system is capable of estimating the reference element behind the vehicle with sufficient accuracy when the laser scanner is placed at the front of it, so the robustness of the control input variables (lateral and angular errors) estimation is improved making it unnecessary to place the sensor on the vehicle roof or to introduce additional sensors.

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“…As road surface features become more and more complicated and critical to traffic safety, Jenny Guo at el. [27] proposed a fully automatic approach for reconstructing road surface features based on the LiDAR technology. Jaselskis et al [28] applied laser scanning to improve transportation projects by measuring the volume of soil and rock and determining road elevations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

Xie¹,

Zhao²,

He³

et al. 2018

Preprint

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Abstract:The application of 3D LiDAR technology has become increasingly extensive in tunnel monitoring due to the large density and high accuracy of the acquired spatial data. The proposed processing method aims at circle tunnels and provides a clear workflow to automatically process raw point data and easily interpretable results to analyze tunnel health state. The proposed automatic processing method employs a series of algorithms to extract point cloud of a single tunnel segment without obvious noise from entire raw tunnel point cloud mainly by three steps: axis acquisition, segments extraction and denoising. Tunnel axis is extracted by fitting boundaries of the tunnel point cloud rejection in plane with RANSAC algorithm. With guidance of axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of tunnel point cloud which corresponds to a single tunnel segment. Then the noise in every single point cloud segment is removed by clustering algorithm twice, based on distance and intensity. Finally, clean point clouds of tunnel segments are processed by effective deformation extraction processor to get ovality and three-dimensional deformation nephogram.

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Automatic reconstruction of road surface features by using terrestrial mobile lidar

Cited by 42 publications

References 18 publications

Use of mobile LiDAR in road information inventory: a review

Use of mobile LiDAR in road information inventory: a review

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

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