Road side detection and reconstruction using LIDAR sensor

Hervieu, Alexandre; Soheilian, Bahman

doi:10.1109/ivs.2013.6629637

Cited by 31 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Curb points are detected by geometrically analysing local points. Angular distance to ground normal map is computed in (Hervieu and Soheilian, 2013) using LiDAR data. It is used for generating feature map and distinguishing 3D curb patterns from other features, such as ground, facades or stairs of higher heights.…”

Section: Introductionmentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in XYZI format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.

show abstract

Section: Introductionmentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…• Road inventory (Pu et al, 2011) • Road surface modelling (Hervieu and Soheilian, 2013a) and quality analysis…”

Section: Contextmentioning

confidence: 99%

Road Orthophoto/DTM Generation From Mobile Laser Scanning

Vallet¹,

Papelard²

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper proposes a pipeline to produce road orthophoto and DTM from Mobile Laser Scanning (MLS). For the ortho, modern laser scanners provide a reflectance information allowing for high quality grayscale images, at a much finer resolution than aerial photography can offer. For DTM, MLS offers a much higher accuracy and density than aerial products. This increased precision and resolution leverages new applications for both ortho and DEM. The first task is to filter ground vs non ground, then an interpolation is conducted to build image tiles from the filtered points. Finally, multiple layers are registered and blended to allow for seamless fusion. Our proposed approach achieves high quality products and scaling up is demonstrated.

show abstract

“…More than one classification criteria can be used to categorize these methods. For one hand, according to sensors being used, road detection method can be categorized into camera-based [1, 4, 6, 7, 12, 14, 15, 21-23, 25, 27, 31, 33, 37-39, 43, 46], Lidar-based [2,9,20,28,35,47,45], and fusion-based [11,19,26,32,[40][41][42]. From the beginning, the camera was used for road detection [1,12,21,33], and has been extended to nowadays [6, 7, 14, 15, 22, 23, 25, 27, 31, 36-39, 43, 46].…”

Section: Related Workmentioning

confidence: 99%

Road detection in image by fusion laser points based on fuzzy SVM for a small ground mobile robot

Yuan

Tang

Zhao

2015

IFS

View full text Add to dashboard Cite

Abstract. Road detection is still full of challenge for a small ground mobile robot with limited load capacity and computing resources which works in complex outdoor environment. This paper proposes a road detection method based on fuzzy support vector machine with on-line updating and retraining strategy. The algorithm extracts multi feature in image and trains a fuzzy support vector machine road classifier off-line by using few training samples. Then it detects road in laser points using a fuzzy clustering method based on maximum entropy principle. After calibrating the camera and laser range finder, and project laser points into the image, the algorithm chooses road samples with high confidence automatically according to range data and designs a rule to retraining the FSVM on-line when needed to improve its environmental adaptability. Experiments in outdoor campus environment indicate that the proposed algorithm is effective.

show abstract

Road side detection and reconstruction using LIDAR sensor

Cited by 31 publications

References 11 publications

Road Surface Detection From Mobile Lidar Data

Road Surface Detection From Mobile Lidar Data

Road Orthophoto/DTM Generation From Mobile Laser Scanning

Road detection in image by fusion laser points based on fuzzy SVM for a small ground mobile robot

Contact Info

Product

Resources

About