{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/371783"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/371783","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Enhancing the Resolution and Predicting the Accuracy of Point Clouds Generated by Handheld 3D Scanners in Construction","abstract":"3D scanning serves as a fundamental element in a range of applications within the architecture, engineering, and construction industry. It provides point clouds that are utilised for construction progress monitoring, scan-to-BIM workflows and construction surveys. Nevertheless, data acquisition relying on terrestrial laser scanners or photogrammetry methods is labor-intensive during both the scanning and post-processing phases. Handheld scanners theoretically present a solution to this challenge due to their potential to significantly reduce on-site scanning efforts and obviate the need for post-processing tasks. However, existing mobile mapping devices are limited in their ability to produce accurate and high-resolution point clouds as compared to terrestrial laser scanners. The primary objective of this thesis is to formulate, implement, and evaluate two methods designed to enhance the resolution and estimate the accuracy of point clouds produced by handheld 3D scanners. The first method is novel in two key ways: (1) it boosts the resolution of a series of sequential sparse lidar scans by fusing them with high-resolution colour images, and (2) it employs these higher-resolution scans to progressively reconstruct a scene. The second method is designed to make real-time predictions on point cloud accuracy, which are based on the estimations of uncertainty levels in SLAM algorithms running in handheld scanners. This method also compares the estimates to the accuracy levels established by surveying standards, displaying the results to the user through colour overlays on the progressively built point cloud, hence enabling user-friendly and real-time assurance of point cloud accuracy. To assess these methods in real-world on-site scenarios, the author of this thesis assembled a unique dataset, ConSLAM, facilitating the evaluation and comparison of SLAM algorithms used by handheld 3D scanners and autonomous robots in a construction setting. The significant contributions of this thesis are primarily threefold: (1) the proposed camera-lidar fusion method increases the point cloud density approximately sixfold and reduces noise by around 11\\%. This results in the improvement in point cloud resolution, which facilitates superior recognition of building elements in point clouds; (2) the introduction of ConSLAM, the world's first dataset which enables the accuracy measurements of SLAM algorithms on construction sites. The research community can also utilise this dataset to measure how the performance of their algorithms changes along with on-site progress; (3) the point cloud accuracy estimation method demonstrates a statistically relevant correlation between accuracy estimations and the actual error in point clouds. This means that the method can flag sections of point clouds with potentially higher spatial error, thereby safeguarding users from making incorrect measurements. To the best of the author's knowledge, this is the first such method of its kind.","abstract_html":"3D scanning serves as a fundamental element in a range of applications within the architecture, engineering, and construction industry. It provides point clouds that are utilised for construction progress monitoring, scan-to-BIM workflows and construction surveys. Nevertheless, data acquisition relying on terrestrial laser scanners or photogrammetry methods is labor-intensive during both the scanning and post-processing phases. Handheld scanners theoretically present a solution to this challenge due to their potential to significantly reduce on-site scanning efforts and obviate the need for post-processing tasks. However, existing mobile mapping devices are limited in their ability to produce accurate and high-resolution point clouds as compared to terrestrial laser scanners. The primary objective of this thesis is to formulate, implement, and evaluate two methods designed to enhance the resolution and estimate the accuracy of point clouds produced by handheld 3D scanners. The first method is novel in two key ways: (1) it boosts the resolution of a series of sequential sparse lidar scans by fusing them with high-resolution colour images, and (2) it employs these higher-resolution scans to progressively reconstruct a scene. The second method is designed to make real-time predictions on point cloud accuracy, which are based on the estimations of uncertainty levels in SLAM algorithms running in handheld scanners. This method also compares the estimates to the accuracy levels established by surveying standards, displaying the results to the user through colour overlays on the progressively built point cloud, hence enabling user-friendly and real-time assurance of point cloud accuracy. To assess these methods in real-world on-site scenarios, the author of this thesis assembled a unique dataset, ConSLAM, facilitating the evaluation and comparison of SLAM algorithms used by handheld 3D scanners and autonomous robots in a construction setting. The significant contributions of this thesis are primarily threefold: (1) the proposed camera-lidar fusion method increases the point cloud density approximately sixfold and reduces noise by around 11\\%. This results in the improvement in point cloud resolution, which facilitates superior recognition of building elements in point clouds; (2) the introduction of ConSLAM, the world's first dataset which enables the accuracy measurements of SLAM algorithms on construction sites. The research community can also utilise this dataset to measure how the performance of their algorithms changes along with on-site progress; (3) the point cloud accuracy estimation method demonstrates a statistically relevant correlation between accuracy estimations and the actual error in point clouds. This means that the method can flag sections of point clouds with potentially higher spatial error, thereby safeguarding users from making incorrect measurements. 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