ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 207–213, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 207–213, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020

  03 Aug 2020

03 Aug 2020

ON-ROAD INFORMATION EXTRACTION FROM LIDAR DATA VIA MULTIPLE FEATURE MAPS

H. Wu1, Z. Xie1, C. Wen1, C. Wang1, and J. Li2 H. Wu et al.
  • 1Fujian Key Laboratory of Sensing and Computing for Smart Cities and the School of Informatics, Xiamen University, Xiamen 361005, China
  • 2Departments of Geography and Environmental Management/Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

Keywords: On-Road Information Extraction, Road Boundary, Road Markings, Road Cracks, Feature Map, LiDAR, Deep Learning

Abstract. On-road information, including road boundaries, road markings, and road cracks, provides significant guidance or warning to all road users. Recently, the on-road information extraction from LiDAR data have been widely studied. However, for the LiDAR data with lower accuracy and higher noise, some detailed information, such as road boundary, is difficult to be extracted correctly. Furthermore, most of previous studies lack an exploration of efficiently extracting multiple on-road information from a single framework. In this paper, we propose a new framework that can simultaneously extract multiple on-road information from high accuracy LiDAR data and can also more robustly extract detailed road boundaries from low accuracy LiDAR data. First, we propose a Curb-Aware Ground Filter to extract ground points with rich curb structure features. Second, we transform the vertical density, elevation gradient and intensity features of the ground points into multiple feature maps and extract multiple on-road information from the feature maps by employing a semantic segmentation network. Experimental results on three datasets with different data accuracy demonstrate that our method outperforms other recent competitive methods.