Abstract
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.
Highlights
On-road information, such as road boundary, road markings, and road cracks, plays an important role in urban construction
Extraction of the on-road information is significant in many applications such as road maintenance (El-Halawany et al, 2012), city planning, intelligent drive assistant systems (Wen et al, 2016), High Definition (HD) map (Ma et al, 2018) and traffic flow monitoring and prediction (Lv et al, 2015)
We propose a multiple feature map-based on-road information extraction framework
Summary
On-road information, such as road boundary, road markings, and road cracks, plays an important role in urban construction. Some detailed onroad information, such as road boundaries, could be extracted from the high accuracy point clouds via the structure feature of curbs (Jaakkola et al, 2008, Yang et al, 2017). We propose a multiple feature map-based on-road information extraction framework. Information (Zai et al, 2017, Wen et al, 2019a), the CurbAware Ground Filter extracts both road surface points and curb points which provide essential structure features for robust road boundary extraction. The double-blind peer-review was conducted on the basis of the full paper
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More From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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