TY - GEN
T1 - Marker-Assisted Structure from Motion for 3D Environment Modeling and Object Pose Estimation
AU - Feng, Chen
AU - Kamat, Vineet R.
AU - Menassa, Carol C.
N1 - Publisher Copyright:
© ASCE.
PY - 2016
Y1 - 2016
N2 - Accurately modeling as-built environments and tracking moving objects' poses are critical for many architecture, engineering, construction, and facility management (AECFM) automation applications. Equally important are the reliability, operating range and cost efficiency of such solutions for their broad deployment in unstructured, dynamic, and sometimes featureless AECFM sites. In this paper, a flexible vision-based technique is developed for accurate, robust, low-cost, and scalable pose estimation and as-built modeling in AECFM applications. This technique combines marker-based pose estimation and structure-from-motion (SfM). In the preparation phase, a sparse set of visual markers are installed in the target environment. During the operation phase, a set of unordered images are taken with a calibrated RGB camera. These images are immediately processed by a SfM system to estimate those markers' poses and generate a sparse point cloud, which can be used by robots or other mobile clients for either moving objects' pose estimation, or dimensional analysis of that environment. Furthermore, for as-built modeling, the RGB camera is replaced by a RGBD camera to create both a dense 3D point cloud and a concise planar model of the environment. Experiments have demonstrated sufficient accuracy (average absolute error within 5 mm over a 9 m scale) of the proposed technique.
AB - Accurately modeling as-built environments and tracking moving objects' poses are critical for many architecture, engineering, construction, and facility management (AECFM) automation applications. Equally important are the reliability, operating range and cost efficiency of such solutions for their broad deployment in unstructured, dynamic, and sometimes featureless AECFM sites. In this paper, a flexible vision-based technique is developed for accurate, robust, low-cost, and scalable pose estimation and as-built modeling in AECFM applications. This technique combines marker-based pose estimation and structure-from-motion (SfM). In the preparation phase, a sparse set of visual markers are installed in the target environment. During the operation phase, a set of unordered images are taken with a calibrated RGB camera. These images are immediately processed by a SfM system to estimate those markers' poses and generate a sparse point cloud, which can be used by robots or other mobile clients for either moving objects' pose estimation, or dimensional analysis of that environment. Furthermore, for as-built modeling, the RGB camera is replaced by a RGBD camera to create both a dense 3D point cloud and a concise planar model of the environment. Experiments have demonstrated sufficient accuracy (average absolute error within 5 mm over a 9 m scale) of the proposed technique.
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U2 - 10.1061/9780784479827.259
DO - 10.1061/9780784479827.259
M3 - Conference contribution
AN - SCOPUS:84976351087
T3 - Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan - Proceedings of the 2016 Construction Research Congress, CRC 2016
SP - 2604
EP - 2613
BT - Construction Research Congress 2016
A2 - Perdomo-Rivera, Jose L.
A2 - Lopez del Puerto, Carla
A2 - Gonzalez-Quevedo, Antonio
A2 - Maldonado-Fortunet, Francisco
A2 - Molina-Bas, Omar I.
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan, CRC 2016
Y2 - 31 May 2016 through 2 June 2016
ER -