Pole-Shaped Object Detection Using Mobile Lidar Data in Rural Road Environments

Yadav, Manohar; Husain, Arshad; Singh, Ajai K.; Lohani, Bharat

doi:10.5194/isprsannals-ii-3-w5-11-2015

Cited by 21 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bounding box is defined by its four vertices. Further, the data points within bounding box are divided into 2D regular square grids (Yadav et al, 2015). Square grid size (m×m) is defined by the user on the basis of the slope of the ground within the grid.…”

Section: 1mentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

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

Self Cite

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: 1mentioning

confidence: 99%

Road Surface Detection From Mobile Lidar Data

Yadav

Lohani

Singh

2018

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…But points from base of vertical objects and low vegetation are still present in the filtered data. Each point of ground segment is selected as seed point and its neighbouring points within radius R are computed using 2D k-d tree neighborhood search technique (Yadav et al, 2015).…”

Section: Ground Points Filteringmentioning

confidence: 99%

Extraction of Ground Surface Along Roadway From Mobile Lidar Data

Yadav

Singh

Lohani

2018

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

Self Cite

View full text Add to dashboard Cite

Abstract. High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.

show abstract

“…There are also spatial analysis methods applied based on grid, voxel or super-voxel. Yadav, et al [16] implemented a three-stage strategy to extract poles. Firstly, point cloud was grouped into 2D grids.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

et al. 2018

View full text Add to dashboard Cite

Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.

show abstract

Pole-Shaped Object Detection Using Mobile Lidar Data in Rural Road Environments

Cited by 21 publications

References 8 publications

Road Surface Detection From Mobile Lidar Data

Road Surface Detection From Mobile Lidar Data

Extraction of Ground Surface Along Roadway From Mobile Lidar Data

Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

Contact Info

Product

Resources

About