A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Robust Alignment of UGV Perspectives with BIM for Inspection in Indoor Environments
https://orcid.org/0000-0003-3491-3281
https://orcid.org/0000-0003-2783-303X
Ensuring the alignment of perspectives between unmanned ground vehicles (UGVs) and Building Information Modeling (BIM) is crucial for the precise retrieval and analysis of BIM-stored information during inspection tasks. However, accumulative localization errors often result in deviations between the viewpoints of UGV cameras and their corresponding representations in BIM at specific waypoints. Therefore, this study introduces a sequential rectification method to correct the UGV’s pose within the BIM environment to ensure seamless alignment of perspectives. By leveraging visual features and geometric strategies in sequence, this method correlates the UGV-captured point cloud data with the BIM, thereby deriving accurate and robust pose rectification. Experimental validation in a featureless indoor environment demonstrated that this method reduced the angle and distance error of reference lines in two-dimensional (2D) views to approximately 2° and 7 pixels, respectively, and the root mean square error (RMSE) of three-dimensional (3D) lines to approximately 17 cm. The validation also demonstrated that the proposed method was particularly robust in correcting the pose and improving the alignment of perspectives between BIM and the UGV, even in cases of significant misalignment. Hence, this study improves the reliability of decisions made by UGVs for indoor inspection when cross-referencing with BIM data.
UGVs have been leveraged to automate inspection tasks, thereby reducing human participation. Generally, these UGVs are programmed to perform analyses by comparing the as-built condition with the as-designed BIM. However, for these tasks to accurately reference the data stored in BIM, it is crucial to have a seamless alignment between the UGV’s perspectives and the as-designed BIM. Unfortunately, due to the accumulation of localization errors, discrepancies often arise between the perspectives in BIM and UGV cameras at waypoints. Such misalignments can compromise the reliability of decisions made for assigned tasks compared with BIM, such as verifying measurements of elements or confirming equipment installation during the project handover phase. Hence, this study, having demonstrated superior performance and robustness, could provide an effective solution to this alignment challenge. By ensuring seamlessly aligned perspectives, this method improves the reliability of UGV inspection assessments when accessing corresponding data stored in BIM.
Robust Alignment of UGV Perspectives with BIM for Inspection in Indoor Environments
https://orcid.org/0000-0003-3491-3281
https://orcid.org/0000-0003-2783-303X
Ensuring the alignment of perspectives between unmanned ground vehicles (UGVs) and Building Information Modeling (BIM) is crucial for the precise retrieval and analysis of BIM-stored information during inspection tasks. However, accumulative localization errors often result in deviations between the viewpoints of UGV cameras and their corresponding representations in BIM at specific waypoints. Therefore, this study introduces a sequential rectification method to correct the UGV’s pose within the BIM environment to ensure seamless alignment of perspectives. By leveraging visual features and geometric strategies in sequence, this method correlates the UGV-captured point cloud data with the BIM, thereby deriving accurate and robust pose rectification. Experimental validation in a featureless indoor environment demonstrated that this method reduced the angle and distance error of reference lines in two-dimensional (2D) views to approximately 2° and 7 pixels, respectively, and the root mean square error (RMSE) of three-dimensional (3D) lines to approximately 17 cm. The validation also demonstrated that the proposed method was particularly robust in correcting the pose and improving the alignment of perspectives between BIM and the UGV, even in cases of significant misalignment. Hence, this study improves the reliability of decisions made by UGVs for indoor inspection when cross-referencing with BIM data.
UGVs have been leveraged to automate inspection tasks, thereby reducing human participation. Generally, these UGVs are programmed to perform analyses by comparing the as-built condition with the as-designed BIM. However, for these tasks to accurately reference the data stored in BIM, it is crucial to have a seamless alignment between the UGV’s perspectives and the as-designed BIM. Unfortunately, due to the accumulation of localization errors, discrepancies often arise between the perspectives in BIM and UGV cameras at waypoints. Such misalignments can compromise the reliability of decisions made for assigned tasks compared with BIM, such as verifying measurements of elements or confirming equipment installation during the project handover phase. Hence, this study, having demonstrated superior performance and robustness, could provide an effective solution to this alignment challenge. By ensuring seamlessly aligned perspectives, this method improves the reliability of UGV inspection assessments when accessing corresponding data stored in BIM.
Robust Alignment of UGV Perspectives with BIM for Inspection in Indoor Environments
https://orcid.org/0000-0003-3491-3281
https://orcid.org/0000-0003-2783-303X
Ensuring the alignment of perspectives between unmanned ground vehicles (UGVs) and Building Information Modeling (BIM) is crucial for the precise retrieval and analysis of BIM-stored information during inspection tasks. However, accumulative localization errors often result in deviations between the viewpoints of UGV cameras and their corresponding representations in BIM at specific waypoints. Therefore, this study introduces a sequential rectification method to correct the UGV’s pose within the BIM environment to ensure seamless alignment of perspectives. By leveraging visual features and geometric strategies in sequence, this method correlates the UGV-captured point cloud data with the BIM, thereby deriving accurate and robust pose rectification. Experimental validation in a featureless indoor environment demonstrated that this method reduced the angle and distance error of reference lines in two-dimensional (2D) views to approximately 2° and 7 pixels, respectively, and the root mean square error (RMSE) of three-dimensional (3D) lines to approximately 17 cm. The validation also demonstrated that the proposed method was particularly robust in correcting the pose and improving the alignment of perspectives between BIM and the UGV, even in cases of significant misalignment. Hence, this study improves the reliability of decisions made by UGVs for indoor inspection when cross-referencing with BIM data.
UGVs have been leveraged to automate inspection tasks, thereby reducing human participation. Generally, these UGVs are programmed to perform analyses by comparing the as-built condition with the as-designed BIM. However, for these tasks to accurately reference the data stored in BIM, it is crucial to have a seamless alignment between the UGV’s perspectives and the as-designed BIM. Unfortunately, due to the accumulation of localization errors, discrepancies often arise between the perspectives in BIM and UGV cameras at waypoints. Such misalignments can compromise the reliability of decisions made for assigned tasks compared with BIM, such as verifying measurements of elements or confirming equipment installation during the project handover phase. Hence, this study, having demonstrated superior performance and robustness, could provide an effective solution to this alignment challenge. By ensuring seamlessly aligned perspectives, this method improves the reliability of UGV inspection assessments when accessing corresponding data stored in BIM.
Robust Alignment of UGV Perspectives with BIM for Inspection in Indoor Environments
J. Comput. Civ. Eng.
Liang, Houhao (author) / Yeoh, Justin K. W. (author) / Chua, David K. H. (author)
2024-07-01
Article (Journal)
Electronic Resource
English
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