A Smart Shoe for building a real-time 3D map

Nguyen, Luan Van; La, Hung Manh; Sánchez, Jesús Gutiérrez; Vu, Tam

doi:10.1016/j.autcon.2016.03.001

Cited by 22 publications

(10 citation statements)

References 19 publications

(24 reference statements)

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“…Portable devices are considered those that are handled by workers. On the other hand, wearable devices can be inferred as those that are next to the body, that is, adhered or in clothes [35] and also as those coupled to personal protective equipment of workers [10,[36][37][38]. First, because they are close to the human body, these technologies must be provided with adequate anatomy and safety, for example, to avoid electric shocks or burns [35].…”

Section: Sensored Construction Sitesmentioning

confidence: 99%

Worker 4.0: The Future of Sensored Construction Sites

Calvetti

Mêda²,

Gonçalves

et al. 2020

Buildings

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The digitalization of the construction industry (CI) has the aim—among others—to raise the bar of overall productivity. The craft workforce is very relevant on the overall value-chain. Therefore, a boost in this dimension impacts the entire sector. There is a gap in proper methodologies to measure and model productivity. Construction 4.0 novelties provide new approaches for its evaluation and progress. This communication presents a review of workforce productivity assessment and delivers methods focusing primarily on craft workers motion monitoring. Products and services opportunities from Construction 4.0 in the spectrum of craft workforce management include support by embedded sensors for data collection that allow near real-time monitoring. The work developed led to the systematization of a framework to standardize craft workers’ motion productivity. The craft workforce motion productivity framework, Worker 4.0, tenders nine processes integrated on a flowchart to streamline task processes assessment and mechanization level. It also sets up a two-handed/two-legged chart system to model craft workers’ activities and operations. The contributions to the body of knowledge are substantiated on the framework creation with the ability to model and assess craft workforce performance. This approach is meant to serve as base point for different stakeholders focusing on skills, efficiency, mechanization and productivity improvements.

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Section: Sensored Construction Sitesmentioning

confidence: 99%

Worker 4.0: The Future of Sensored Construction Sites

Calvetti

Mêda²,

Gonçalves

et al. 2020

Buildings

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“…MLS data with high point density allows the extraction and 3D reconstructions of buildings for building information modeling (BIM) [58][59][60][61][62]. MLS data have been used to identify building shape, structure, boundary, façade, roof, and outdoor/indoor environments [12,14,30,[63][64][65][66] and street-level building change detection [32,67], and thus have gained attention in the fields of architecture, engineering, and construction [68,69]. The automatic reconstruction method of permanent structures, such as walls, floors and ceilings, combined with feature primitive extraction and graph-cut formulation.…”

Section: Building Information Modelingmentioning

confidence: 99%

A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

et al. 2019

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Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.

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“…Without this reflection, the PrimeSense camera cannot be relied upon, so the robotic system is equipped with two cameras to complement each other and allow for robust indoor navigation in the presence of varying objects and object densities. We can further enhance the reliability of the robot indoor/outdoor navigation by integrating laser range finders or LiDAR with 3D mapping technique (Nguyen, La, Sanchez, & Vu, ; Nguyen & La, ).…”

Section: Navigationmentioning

confidence: 99%

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

Gibb

et al. 2018

Journal of Field Robotics

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Civil infrastructure inspection is crucial to maintaining the quality of that infrastructure, which has a great impact on the economy. Performing this inspection is costly work that requires workers to be trained on how to use varying technologies, which can be error prone when performed manually and can result in damage to the infrastructure in some cases. For this reason, nondestructive evaluation (NDE) sensors are preferred for civil infrastructure inspection as they can perform the necessary inspection without damaging the infrastructure. In this paper, we develop a fully autonomous robotic system capable of real-time data collection and quasi-real-time data processing. The robotic system is equipped with several NDE sensors that allow for a sensor fusion method to be developed that successfully minimizes inspection time while performing adequate inspection of areas that require more in-depth data to be collected. A detailed discussion of the inspection framework developed for this robotic system, and the dual navigation modes for both indoor and outdoor autonomous navigation is presented. The developed robotic system is deployed to inspect several infrastructures (e.g., parking garages, bridges) at and near by the University of Nevada, Reno campus. K E Y W O R D Sconcrete inspection, field robots, non-destructive inspection SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Gibb S, La HM, Le T, Nguyen L, Schmid R, Pham H. Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection.

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A Smart Shoe for building a real-time 3D map

Cited by 22 publications

References 19 publications

Worker 4.0: The Future of Sensored Construction Sites

Worker 4.0: The Future of Sensored Construction Sites

A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

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