Method for extracting indoor parking lot ground marking elements based on handheld laser scanning point cloud

doi:10.13474/j.cnki.11-2246.2023.0348

Abstract

Abstract: In order to meet the demand for rapid mapping of indoor parking lots, a method is proposed for extracting ground marking features in indoor parking lots based on handheld LiDAR point clouds. Firstly, to reduce the memory space requirements for feature extraction, the entire point cloud is divided into regular grids. Then, the RANSAC plane fitting method is used to extract the ground point cloud within each grid. In order to extract ground marking features, a ground image is generated based on the ground point cloud. On this basis, the BiSeNet network is employed for semantic segmentation of different marking features, such as lane lines, parking lines, and directional arrow markings, to obtain the corresponding pixels. For lane lines and parking lines, a line extraction method based on Hough transform is used, while for ground arrow markings, a template matching method is applied for extraction. Experimental results demonstrate that the proposed method can quickly extract both structural elements and marking features from scanned data, significantly reducing manual mapping workload and improving the efficiency of indoor parking lot mapping.

Key words: handheld 3D laser scanning, parking garage, ground marking elements, 3D point cloud, deep learning

CLC Number:

P237

LIU Yuntong, HUANG Jinting, WANG Jiayao. Method for extracting indoor parking lot ground marking elements based on handheld laser scanning point cloud[J]. Bulletin of Surveying and Mapping, 2023, 0(11): 173-176.

References

[1] 张微,华一丁,臧晨,等. 面向室内停车场的自主代客泊车关键技术研究[J]. 汽车电器,2019(8): 6-8.
[2] 樊培毅,陈阳,程森森,等. 基于WiFi室内定位的地下停车场智能导航系统[J]. 物联网技术,2016,6(4): 31-34.
[3] 李效超,王智,孙晓丽,等. 手持式移动三维激光扫描仪在地下空间普查中的应用研究[J]. 城市勘测,2020(2): 62-65.
[4] 李毅,徐超,廖开星,等. 手持式三维激光扫描仪在工业构件质量检测中的应用[J]. 测绘通报,2019(8): 102-105.
[5] 刘联红,饶建荣,洪伟. 手持三维激光扫描测量合拢管的改进方案[J]. 广东造船,2023,42(2): 62-64.
[6] 汪建波,孙亦东. 手持式三维激光扫描仪在文物保护中的应用[J]. 江西测绘,2017(1): 47-50.
[7] SCHNABEL R,WAHL R,KLEIN R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum,2007,26(2): 214-226.
[8] LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553): 436-444.
[9] SHELHAMER E,LONG J,DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4): 640-651.
[10] BADRINARAYANAN V,KENDALL A,CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12): 2481-2495.
[11] YU Changqian,WANG Jingbo,PENG Chao,et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[C]//Proceedings of 2018 European Conference on Computer Vision. Cham: Springer,2018: 334-349.
[12] HOU Jiawei,CHEN Qi,CHENG Yurong,et al. SUPS: a simulated underground parking scenario dataset for autonomous driving[C]//Proceedings of 2022 IEEE International Conference on Intelligent Transportation Systems (ITSC).[S.l.]:IEEE,2022: 2265-2271.
[13] 吴桐. 基于改进霍夫变换线段检测算法的实现和应用[D]. 济南: 山东大学,2018.
[14] 王棚飞,刘宏哲,袁家政,等. 一种简易有效的地面导向箭头检测识别算法[C]//中国计算机用户协会网络应用分会2014年第十八届网络新技术与应用年会.昆明:[s.n.],2014.

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