Robotic Mapping of Cultural Heritage Sites

Borrmann, Dorit; Hess, Rob; Houshiar, Hamidreza; Eck, Daniel; Schilling, Klaus; Nüchter, Andreas

doi:10.5194/isprsarchives-xl-5-w4-9-2015

Cited by 24 publications

(12 citation statements)

References 13 publications

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“…As a consequence of this, a huge amount of data has to be processed after several scans, with the sole aim of recognizing furniture [8], or extracting frontiers. 3D mapping in References [7,9,10,12,19,24], and robot localization/navigation [10,25,26] and digitization [5,27] are research lines in which the data redundancy problem is not considered in the data acquisition stage. However, some redundancy in the collected data can also be useful, for instance to increase robustness or to increase the probability of completeness of the model.…”

Section: Utility and Redundancy Of The Datamentioning

confidence: 99%

“…With regard to the interiors of buildings, some systems are constrained to the scanning of corridors [10], which are very simple shapes. Most works deal with scenes composed of a corridor to which several rooms are connected [5,6,11,12,[24][25][26][27]30]. In some cases, the mobile scanning system moves along the corridor, enters the room in order to take 3D data, leaves the room, and goes back to the corridor [5,12].…”

Section: Geometrymentioning

confidence: 99%

“…Many of the systems work in rectangular rooms [5,6,12,15,18,24,25,27,30], and a few in free-shape interiors [11]. Iocchi et al [11] generated a multilevel 2D-map with which to generate not-necessarily orthogonal structural elements.…”

Section: Geometrymentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

2019

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Mobile scanning systems are being used more and more frequently in industry, construction, and artificial intelligent applications. More particularly, autonomous scanning plays an essential role in the field of the automatic creation of 3D models of building. This paper presents a critical review of current autonomous scanning systems, discussing essential aspects that determine the efficiency and applicability of a scanning system in real environments. Some important issues, such as data redundancy, occlusion, initial assumptions, the complexity of the scanned scene, and autonomy, are analysed in the first part of the document, while the second part discusses other important aspects, such as pre-processing, time requirements, evaluation, and opening detection. A set of representative autonomous systems is then chosen for comparison, and the aforementioned characteristics are shown together in several illustrative tables. Principal gaps, limitations, and future developments are presented in the last section. The paper provides the reader with a general view of the world of autonomous scanning and emphasizes the difficulties and challenges that new autonomous platforms should tackle in the future.

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Section: Utility and Redundancy Of The Datamentioning

confidence: 99%

Section: Geometrymentioning

confidence: 99%

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Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

2019

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“…In robotics, SLAM is a popular method for enabling a robot to estimate its current position and orientation from a map of the environment created by LiDAR or cameras. The problem with this approach is that most SLAM systems are commanded manually; humans must decide where to go and how to perform complete scanning of a large site [14], [15] 3 Objective…”

Section: Scan Planning and Autonomous Scanningmentioning

confidence: 99%

As-is Geometric Data Collection and 3D Visualization through the Collaboration between UAV and UGV

Kim¹,

Park²,

Cho³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The characteristics of dynamic construction sites increase the difficulty of collecting the high-quality geometric data necessary to achieve construction management activities. This paper introduces a new autonomous framework for 3D geometric data collection in a dynamic cluttered environment using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). This method first deploys UAV to collect photo images of a site and builds a point cloud of the 3D terrain of the site, including obstacle information. A mesh grid is then created from the UAV-generated point cloud, and simulation for laser-scan planning is conducted to determine the stationary laser-scan positions at which a mobile robot can collect data with less occluded views while capturing all crucial geometric information. Finally, optimal paths for the UGV to navigate among the estimated scan positions are generated. Promising test results regarding data accuracy and collection time show that the proposed collaborative UAV-UGV approach can significantly reduce human intervention and provide technologies for enabling construction site to be frequently monitored, updated, and analyzed for timely decision-making.

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“…Although these instruments are highly accurate, the procedure is very time-consuming [1][2][3]. Their use in some scenes is moving to laser scanning devices, which are being increasingly adopted as default instruments for the collection of 3D data in large-scale civil infrastructures [4] and cultural heritage sites [5,6].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos

Díaz-Vilariño

Balado

et al. 2018

IJGI

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This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

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Robotic Mapping of Cultural Heritage Sites

Cited by 24 publications

References 13 publications

Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

As-is Geometric Data Collection and 3D Visualization through the Collaboration between UAV and UGV

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

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