Automatic Extraction of Road Markings From Mobile Laser-Point Cloud Using Intensity Data

Abstract

ABSTRACT:With the development of intelligent transportation, road's high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.

Automatic Detection and Vectorization of Linear and Point Objects in 3d Point Cloud and Panoramic Images From Mobile Mapping System

“…(Smadja et al, 2010, Wu et al, 2017, Jung et al, 2019 implement Random Sample Consensus algorithm (RANSAC) to perform the road extraction. A scan-line structure, based on the GNSS timestamp or scanning angle field, is used by (Yu et al, 2015, Yao et al, 2018 to realize the segmentation and the following extractions steps. The altitude along the scan-line is analyzed to identify the road's points.…”

“…Because the laser pulse intensity decrease with the increase of the scanning range and the incident angle between the scanner and the scanned objects, (Jaakkola et al, 2008, Kumar et al, 2014, Yu et al, 2015, Soilán et al, 2017 adapt their threshold using these information. Others adaptative thresholding methods have been proposed by different authors (Cheng et al, 2017, Yao et al, 2018 to deal with the inhomogeneous intensity observed in the point clouds.…”

“…It allows to reconstruct the traffic lane. (Yao et al, 2018) performed a skeletonization followed by a template matching method to identify specific markings like arrows, text or other specifics road markings.…”

Automatic Extraction and Recognition of Road Markings Based on Vehicle Laser Point Cloud

“…Suppose that TP(true positive), FN(false negative), and FP(false positive) represent the counts of the pixels which are correctly, defectively, redundantly extracted respectively in the process of automatic extraction of road markings. The manual annotation result is assessment reference (Yao et al, 2018) and (Cheng et al, 2017) is calculated by using the method proposed by Cheng. The intensity image is processed with adaptive threshold method by different parameters of s and t according the equation(16~18).…”

“…Road marking has higher reflection rate compared to the asphalt. Therefor, road marking can be extracted based on intensity differences between road marking and asphalt road surface (Yao et al, 2018;Yang et al, 2012). A.…”