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一种基于树莓派的用于坑洼三维重建与测绘的自动化系统。

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping.

作者信息

Bruno Salvatore, Loprencipe Giuseppe, Di Mascio Paola, Cantisani Giuseppe, Fiore Nicola, Polidori Carlo, D'Andrea Antonio, Moretti Laura

机构信息

Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.

AIPSS Associazione Italiana Professionisti Sicurezza Stradale, Piazza del Teatro di Pompeo 2, 00186 Rome, Italy.

出版信息

Sensors (Basel). 2023 Jun 24;23(13):5860. doi: 10.3390/s23135860.

DOI:10.3390/s23135860
PMID:37447710
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10347019/
Abstract

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole's shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers' exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers' and road users' health and safety.

摘要

修复坑洼是市政当局的一项任务，以防止道路使用者受到严重伤害和车辆受损。本研究提出了一种低成本、高性能的坑洼监测系统，用于维护城市道路。作者开发了一种基于摄影测量技术的方法来预测坑洼的形状和体积。通过连接到树莓派4 B型的树莓派相机模块3拍摄的一组重叠二维图像，已用于创建坑洼三维模型。基于树莓派的配置已安装在一个自主遥控机器人（在欧洲InfraROB项目中开发）上，以减少工人在调查活动中暴露于实时交通的风险，并使过程自动化。摄影测量处理软件的输出已通过设置为地面真值的实验室测试进行验证；试验在由沥青混合料制成的瓦片上进行，再现真实的坑洼。全球定位系统（GPS）和地理信息系统（GIS）技术使在地图上可视化坑洼成为可能，显示有关其中心、体积、回填材料和相关图像的信息。十次现场测试验证了该系统不仅在实验室，而且在不受控制的环境中也能工作。结果表明，考虑到建筑工人和道路使用者的健康与安全，该系统是监测道路坑洼的一个有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/f1698928b69f/sensors-23-05860-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/594806c99bf2/sensors-23-05860-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/605323b23897/sensors-23-05860-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/f1698928b69f/sensors-23-05860-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/43f252cdc9ab/sensors-23-05860-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/9fe0419ef796/sensors-23-05860-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/0e169f992c67/sensors-23-05860-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/b3790ac7879c/sensors-23-05860-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/ce81a1d00004/sensors-23-05860-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/f4e548d834ae/sensors-23-05860-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/16a17a525932/sensors-23-05860-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/594806c99bf2/sensors-23-05860-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/605323b23897/sensors-23-05860-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6853/10347019/f1698928b69f/sensors-23-05860-g011.jpg

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2
Development of a GIS-Based Methodology for the Management of Stone Pavements Using Low-Cost Sensors.基于 GIS 的低成本传感器在石质铺地管理中的应用方法开发。
Sensors (Basel). 2022 Aug 31;22(17):6560. doi: 10.3390/s22176560.
3
Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks.验证一种用于城市道路网络的低成本路面监测惯性系统。
Sensors (Basel). 2021 Apr 30;21(9):3127. doi: 10.3390/s21093127.
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Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor.基于智能手机加速度传感器的异常路面识别。
Sensors (Basel). 2020 Jan 13;20(2):451. doi: 10.3390/s20020451.
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