An Interactive Model-Driven Path Planning and Data Capture System for Camera-Equipped Aerial Robots on Construction Sites

Ibrahim, Amir; Roberts, Dominic; Golparvar-Fard, Mani

doi:10.1061/9780784480847.015

Cited by 13 publications

(6 citation statements)

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“…These challenges are only reported after post-processing of the collected data and reconstructing the 3D as-built geometries. To improve the visual quality of the data and shorten their acquisition duration, change-driven visual quality metrics are utilized to provide visual quality feedback for the data collection plans before execution (Ibrahim, Roberts, Golparvar-Fard & Bretl 2017). Light Detection and Ranging (LiDAR) sensors are also used to collect high quality 3D as-built geometries.…”

Section: Related Workmentioning

confidence: 99%

“…Global Navigation Satellite System (GNSS) is used to localize the autonomous platform by triangulating the position of the mounted GNSS receiver with respect to multiple detected satellites. Such technology is mainly used for outdoor data collection using camera-equipped drones (Tahar & Kamarudin 2016, Ibrahim, Roberts, Golparvar-Fard & Bretl 2017, Rakha & Gorodetsky 2018, Freimuth & König 2018 or multi-sensory autonomous ground rovers (Kim et al 2003, Ramanagopal & Ny 2016, Haala et al 2008, Srinivasan Ramanagopal et al 2018. To improve the accuracy of localization and mapping, fiducial tags are usually placed at pre-defined locations to provide ground truth survey points.…”

Section: Gnss-based Platformsmentioning

confidence: 99%

See 1 more Smart Citation

BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform

Ibrahim¹,

Sabet²,

Golparvar-Fard³

2019

Computing in Construction

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Reconstructing a complete and accurate 3D representation of indoor construction scenes is an important step towards automated visual monitoring of construction projects. For fast access to construction's as-built visual data, construction drones are programmed to autonomously navigate the outdoor space and collect the data. However, due to limited satellite signal indoors, ground rovers provide safer and more reliable autonomous navigation in the narrow cluttered indoor space. In this paper we present a novel pipeline for 4D BIM-driven mapping of the as-built state of indoor construction site. 2D Light Detection and Ranging (LiDAR) sensors are mounted on an Unmanned Ground Vehicle (UGV) for Simultaneous Localization and Mapping (SLAM). The developed method consists of (1) BIMdriven data collection planning; (2) automatic mission navigation; (3) LiDAR data collection and (4) dynamic obstacle avoidance. Experiments show the applicability of the developed data collection strategy and the improved safety of automatic mission execution using UGVs.

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Section: Related Workmentioning

confidence: 99%

Section: Gnss-based Platformsmentioning

confidence: 99%

BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform

Ibrahim¹,

Sabet²,

Golparvar-Fard³

2019

Computing in Construction

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“…Previous research endeavors have investigated the usage of a priori-such as BIMs-to create, communicate, and evaluate the quality and safety of reality capture plans (Ibrahim, Roberts, et al, 2017;Y. H. Lin et al, 2013;Taneja et al, 2016).…”

Section: Methods To Evaluate Reality Capture Plansmentioning

confidence: 99%

Metrics and methods for evaluating model‐driven reality capture plans

Ibrahim

Golparvar-Fard

El‐Rayes

2021

Computer aided Civil Eng

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This paper presents new metrics and methods for evaluating the quality of reality capture plans-commonly used to operate camera-mounted unmanned aerial vehicles (UAVs) or ground rovers-for construction progress monitoring and inspection of as-is conditions. Using 4D building information model (BIM) or 3D reality model as a priori, these metrics provide feedback on the quality of a plan (within a few minutes), accounting for resolution, visibility, accuracy, completeness of the capture, and satisfying battery capacity and line-of-sight requirements. A cloud-based system is introduced to create and optimize UAV/rover missions in the context of prior model. Results from real-world construction data sets demonstrate that the proposed metrics offer actionable insights into the accuracy and completeness of reality capture plans. Additionally, a capture plan-with a combination of canonical and noncanonical camera viewsthat satisfies the introduced metrics is statistically correlated with the quality of reconstructed reality. These metrics can improve computer-vision progress monitoring and inspection methods that rely on the construction site's appearance and geometry.

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“…A technique was introduced to generate the UAV waypoints for the outdoor inspection of user-selected elements (Freimuth and König 2016). A user interface was designed to provide assessments of user-specified outdoor inspection routes (Ibrahim et al 2017); the technique evaluated the completeness of 3D point clouds and the route's proximity to structure based on the location of user-selected waypoints.…”

Section: Related Workmentioning

confidence: 99%

UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling

Hamledari

Davari

Sajedi

et al. 2018

Construction Research Congress 2018

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Inspection planning is a primary element of computer vision-and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV's limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.

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An Interactive Model-Driven Path Planning and Data Capture System for Camera-Equipped Aerial Robots on Construction Sites

Cited by 13 publications

References 0 publications

BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform

BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform

Metrics and methods for evaluating model‐driven reality capture plans

UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling

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