Systems and methods for post construction validation

Abstract

A method and system are described for verifying site modifications using a drone system. The method involves generating an initial CAD 3D model using drone-captured data, altering it to reflect planned changes, and employing augmented reality to overlay modification plans on live video after site modifications to determine compliance with the as-planned design.

Description

Systems and Methods for Post Construction ValidationFIELD OF THE INVENTION

[0001] The present disclosure relates to the field of digital image processing, specifically concerning the analysis and editing of digital images through computational methods. The technology encompasses methodologies and systems for altering, enhancing, and interpreting visual data within various software applications.BACKGROUND

[0002] In the realm of construction and infrastructure development, traditional methodologies for evaluating site modifications often involve delayed assessments that can lead to considerable challenges. Construction work, once completed, frequently undergoes a review process that does not occur immediately. This latency in evaluation stems from the logistical complexities of reconvening construction teams and equipment for assessment purposes once the primary work is concluded. Consequently, any defects or deviations from planned specifications that manifest post-construction may remain undetected until substantial site usage reveals these issues, delaying necessary corrective measures.

[0003] Such delays in identifying and addressing construction defects or inconsistencies are problematic, as the requisite construction teams and resources are no longer stationed on-site. As a result, rectifying these issues often incurs additional costs, both in terms of mobilizing the necessary labor and equipment and extending project timelines. Moreover, the detection of errors post-completion can lead to disruptions in established operations, impacting structural integrity and usability of the site in question.

[0004] The financial and operational inefficiencies introduced by delayed identification of construction discrepancies underscore the need for more immediate and precise verification methodologies. The current landscape lacks an effective system whereby the accuracy and adherence of constructed modifications to initial plans can be assessed in near real-time, thereby enhancing project oversight and minimization of repair costs.SUMMARY

[0005] The present invention pertains to methods and systems for verifying the modifications of a site utilizing surveys by a drone system, which facilitates accurate assessments of construction or modification work. In one aspect, the method involves generating computer- aided design (CAD) 3D models derived from data captured by a drone, allowing the comparison of as-planned and as-built conditions to ensure compliance with intended modifications. In addition, initial data captured enables the generation of an As-ls CAD 3D model, for which desired site modifications can be edited.

[0006] One object of the system is to enhance the efficiency and precision of site modification evaluations by incorporating advanced data capturing techniques such as Real Time Kinematic (RTK) positioning, which achieves high data accuracy. This advancement aids in the streamlined verification process, particularly when modifications include adding, removing, modifying, or repairing site elements.

[0007] In an embodiment, the method encompasses utilizing computational design automation tools for modifying initial CAD models, resulting in an as-planned CAD model. Furthermore, after completing the modification work, a third CAD model representing the as-built site is generated, with data selectively captured from modified areas. The comparison between the as-planned and as-built models is facilitated by detecting deviations and issuing reports that approve modifications or list rejections.

[0008] Yet another aspect of the disclosed technology involves a system wherein a drone equipped with high-resolution imaging and geospatial data sensors captures site data. The system includes modules for CAD 3D model generation and comparison, and may utilize machine learning to enhance model accuracy. The system integrates a user interface for displaying comparisons, potentially incorporating augmented reality (AR) for overlaying CAD models onto live video feeds.

[0009] Yet another object of the technology is to provide automated reporting capabilities, producing discrepancy reports that summarize deviations and support ongoing assessments through partial reporting features. This system allows evaluation of specific infrastructure types, such as mobile network communication towers, ensuring modifications adhere to infrastructure constraints.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Fig. l is a flowchart illustrating the steps involved in verifying site modifications using a drone system.

[0011] Fig. 2 illustrates a schematic diagram of a system for verifying site modifications using a drone system. The diagram depicts various components including the drone configured with sensors to capture data from the site, a modification module for altering initial CAD 3D models, a data capture module for post-modification surveying, and an augmented reality module for overlaying modification plans onto a live video feed. The system aims to determine the fidelity of site modifications against planned alterations.DETAILED DESCRIPTION

[0012] In one aspect, the present invention relates to a method for verifying modifications of a site using a drone system. The method involves several distinct steps that utilize drone-captured data to facilitate accurate assessments of site modifications through computer-aided design (CAD) modeling.

[0013] Fig. 1 shows a flowchart illustrating a method for verifying modifications of a site using a drone system. The process begins at step 100, which marks the start of the procedure. At step 110, an initial computer-aided design (CAD) 3D model is generated. The data for the CAD model generation can be obtained from any source, including but not limited to, using data captured by a drone, e. This step ensures that the model accurately represents the existing site before any modifications are planned.

[0014] Proceeding to step 120, the initial (as-is) CAD 3D model is modified to incorporate desired changes, resulting in a second (as-planned) CAD 3D model. This modification reflects the as-planned condition of the site once the desired modifications are implemented. At step 130, after the modification work is completed, the modified site is surveyed using a drone to capture updated data.

[0015] At step 140, augmented reality (AR) components of the second CAD 3D model are superimposed over the live video feed from the drone. This step allows for a visual comparison to ascertain whether the site modifications align with the planned design. In step 150, a decision is made to determine if the modification work has been executed according to the plan. The process then concludes at step 160, which signifies the end of the verification procedure.

[0016] In one embodiment, a method for verifying modifications of a site using a drone system encompasses several procedural phases to ensure accurate evaluation of site alterations. This method begins with generating an initial computer-aided design (CAD) 3D model of the existing site. For instance, this initial model is produced using data captured by a drone equipped with high-resolution imaging and geospatial sensors. The generated model represents the current, pre-modification state of the site, establishing a foundational baseline for subsequent alterations.

[0017] This initial CAD 3D model is subsequently modified to integrate desired changes reflective of planned site modifications, generating a second CAD 3D model that encapsulates the as-planned condition of the site. Modifications may involve diverse activities such as the addition, removal, or alteration of structural elements tailored to specific project objectives. Utilizing computational design automation tools, the transition from the initial to the as-planned CAD 3D model is achieved with precision, ensuring adherence to design criteria.

[0018] Upon the completion of modification work, a survey is conducted utilizing a drone. This survey captures updated site data, allowing for an aerial perspective on the executed work. The importance of this step lies in the collection of accurate and detailed data necessary for ensuring the site modifications meet the planned specifications, including any fault tolerance criteria.

[0019] A notable aspect of this method involves superimposing via Augmented Reality (AR) parts of the second CAD 3D model that represent areas intended for inspection on a video feed from the drone, preferably but not mandatory, a live video feed. For example, as the drone navigates over the site, this AR overlay provides an immediate and interactive visual comparison, allowing stakeholders or machine tools to assess the real-time fitment of the executed modifications against the as-planned model. As used herein, the term "real-time" refers to processing, computation, or communication that occurs with a delay that is sufficiently short to maintain practical usability within the intended application. Real-time may include operations performed with minimal latency or those executed in near real-time, where minor delays do not materially impact the effectiveness of the system.

[0020] In deciding whether the modification work has been executed according to plan, any detected deviations from planned design specifications are evaluated, including fault tolerance criteria. The decision process may involve comparing dimensions, alignments, and spatial configurations, determining the congruence of as-built conditions with intended design parameters. An automated decision tool may be deployed in some embodiments to facilitatethis assessment, leveraging software or machine learning algorithms to enhance accuracy and analysis speed. The automated decision tool can be deployed exclusively or in conjunction with a human inspection.

[0021] For instance, in an embodiment applied to a mobile network communication tower, the drone captures data both before and after structural modifications, including the addition or realignment of antennae. The use of AR aids in rapidly detecting discrepancies in alignment or height, ensuring that all modifications conform to regulatory and operational standards.

[0022] In various applications, this method can be exercised in multiple industries such as construction projects, infrastructure development, and telecommunications, providing a robust framework for quickly verifying site modifications effectively and accurately, typically shortly after the modification work has ended. Each phase of the process contributes to a comprehensive evaluation protocol that enhances project oversight and supports compliance with design intentions, culminating in the accurate verification of site alterations.

[0023] In practice, the data captured by the drone comprises not only high-resolution images but also high-accuracy geospatial information, for example, leveraging Real Time Kinematic (RTK) positioning technology or any other similar high-accuracy technology. The integration of RTK positioning enhances the precision of the geospatial data, yielding positioning accuracy potentially within a margin of approximately 3 centimeters. Such precision is instrumental in ensuring the fidelity of both the initial and subsequent CAD 3D models, thereby reinforcing the reliability of the site modification verification process.

[0024] This method presents a systematic approach to site modification verification, utilizing drone technology and robust CAD modeling to achieve a detailed comparison of pre- and postmodification states, thus facilitating enhanced project oversight and adherence to design criteria.

[0025] In some embodiments, the invention benefits from the utilization of high accuracy positioning, such as Real Time Kinematic (RTK) positioning to enhance geospatial data accuracy in the method for verifying modifications of a site using a drone system. RTK positioning is applied to refine the locational precision of the data captured by the drone, thereby improving the fidelity of the three-dimensional CAD models generated during the assessment of the site modifications.

[0026] In one example, the drone is equipped with an RTK-enabled GPS unit that communicates with a terrestrial base station to process the spatial coordinates with a relativeaccuracy margin of approximately 3 centimeters. This system adjusts for atmospheric interference, clock drift, and satellite ephemeris errors by processing both the GPS signals received by the drone and the reference signals from the stationary base station. Such a configuration ensures that the geographic data annotated on the CAD models accurately corresponds to the physical features of the site under review.

[0027] Consider the implementation of this method for assessing modifications to a construction site. Here, the drone deployed for aerial surveying captures images and geospatial information of the terrain before and after construction activities. The RTK system allows for the real-time correction of any positional discrepancies detected in the raw GPS data, thereby providing an exceptional level of detail in the generated CAD models.

[0028] In another example, the RTK capability facilitates the precise identification and measurement of structural elements within the site, such as road alignments, building footprints, and existing infrastructure. By employing RTK positioning, structural changes can be accurately monitored, reducing the potential for errors in the comparison between the as- planned and as-built CAD 3D models.

[0029] Furthermore, the RTK technology is utilized in scenarios where high-density urban environments, with potential sources of signal reflection and interference, present challenges to conventional GPS solutions. By leveraging RTK positioning, the data integrity in closely built-up areas can be maintained, providing reliable input for CAD-based analysis of site modifications.

[0030] In some embodiments, the method comprises the use of computational design automation tools to implement desired changes to the initial computer-aided design (CAD) 3D model of the existing site. The computational design automation tools are employed after the initial CAD model is established, offering enhanced precision and efficiency in modifying the CAD model to reflect planned changes. These tools facilitate the adjustment of the initial model to create a second CAD model, representing the as-planned condition of the site.

[0031] The process of modifying the initial CAD model involves a series of automated design operations that incorporate data inputs specifying the intended modifications. Utilizing design automation tools, alterations are applied to the model, which may include modifications such as the addition, alteration, or removal of structural elements. These operations are supported by the automation tools' ability to interpret and process geometrical data while conforming to predetermined design criteria.

[0032] Automated design tools assist in streamlining the modification phase by allowing for interactive and iterative design changes, enabling the seamless integration of complex adjustments within the CAD environment. The automation tools utilize algorithms to optimize and validate the modifications, ensuring the modified aspects of the model adhere to regulatory and structural standards. This process results in the production of a second, modified CAD 3D model that accurately depicts the prospective alterations as planned for the site.

[0033] Furthermore, the computational design automation tools are configured to support multiple design formats and integrate with other digital engineering software, enabling a versatile approach to model alteration. The use of such tools reduces the potential for human error, enhances the speed of transition from initial to modified model, and maintains consistency in the representation of modifications across different stages of the project lifecycle. Through the employment of these advanced computational tools, the method achieves an efficient and accurate method for updating the initial CAD model in response to planned site modifications.

[0034] In one illustrative embodiment, the method for verifying modifications of a site, utilizing a drone system, includes specific operations concerning the changes to the identified location. The alterations to the site can feature a range of activities, including but not limited to: the addition of structures or sections to existing features, the removal of existing structural elements, repairs or refurbishments of current site components, or any combination of these modifications. These operations are conducted to meet specific renovations, construction requirements, or other planned modification efforts reflected in the as-planned CAD model of the site.

[0035] During the process, the initial CAD 3D model serves as the base digital representation of the site in its pre-modification state. The modifications are introduced into this model, facilitating the creation of a secondary CAD model that incorporates these planned changes. This as-planned CAD model accurately demonstrates the intended outcome of the modification work.

[0036] Superimposing via Augmented Reality (AR) parts of the second CAD 3D model over the video feed captured by a drone serves as a critical means for inspecting designated areas of a site. This advanced technique provides a comparative visual overlay, allowing stakeholders to assess the correspondence of on-site modifications against the as-planned designs, enhancing accuracy and immediacy in project evaluation.

[0037] In one embodiment, the process begins by employing a drone equipped with a high- resolution AR-compatible camera to capture live video footage of the site undergoing modification. The second CAD 3D model, which incorporates the planned changes, is digitally overlaid onto this live feed. For instance, as the drone navigates above a construction site, the AR overlay projects the as-planned structural elements, such as beam placements, roof profiles, or wall alignments, onto the live video captured by the drone. This configuration allows engineers and project managers to visually ascertain whether the constructed features align with the design specifications set forth in the as-planned CAD model.

[0038] In another embodiment, infrastructure projects, such as roadway expansions, utilize AR overlays to ensure conformities, such as newly paved lanes or curb alignments, with the intended design. The live AR feed may highlight deviations in road curvature or lane width, enabling site personnel to undertake immediate corrective measures and sustain adherence to traffic and safety guidelines.

[0039] In some embodiments, the utilization of an automatic tool for modification verification via Augmented Reality (AR) encompasses several technical aspects that enhance precision and efficiency in ensuring compliance with the as-planned design. A key component of this system is the integration of software capable of analyzing CAD models and translating them into AR overlays that can be directly compared to the physical site conditions as captured by the drone system.

[0040] In practice, the automatic tool operates by leveraging algorithms that process the second CAD 3D model, corresponding to the as-planned modifications, and generate a visual overlay for AR-based comparison. This overlay is then projected onto a real-time or recorded video feed from the drone system as it navigates over the site. The tool can process deviations between the planned and actual attributes of the site, such as dimensions, alignments, and spatial configurations, which may have occurred during modification work.

[0041] Tolerance levels are an essential factor in this verification process. These levels are preset parameters delineating acceptable deviation margins within which the modifications are deemed compliant. For example, in the context of a construction site, tolerance levels may account for permissible variance in wall alignment or roof position, specified in millimeters or centimeters, based on engineering or architectural standards. Automated detection algorithms continuously compare the AR overlay with the actual structure as seen through the drone's camera, ensuring that any deviations fall within these defined tolerances.

[0042] An illustrative example involves the assessment of structural modifications in infrastructure projects, such as roadway expansions. Here, an automatic tool calculates the road alignment and surface elevation against the as-planned CAD model. The AR overlay aids in visualizing these parameters on the captured video feed, while the tool flags any discrepancies exceeding the prescribed tolerance levels, for example, a deviation exceeding ±5 centimeters in road surface elevation.

[0043] In telecommunications infrastructure, the automatic tool becomes instrumental in verifying modifications of mobile network communication towers. The AR overlay visualizes the placement and alignment of newly installed antennae over the drone feed, with an automatic comparison tool detecting deviations against a tolerance level of, for instance, ±2 centimeters in antenna height or alignment. Such precision is crucial to adhere strictly to operational and safety standards. In addition, the angle deviation for the antenna heading and tilt can be inspected to meet, for example, a tolerance of ±1 degrees.

[0044] Furthermore, in an architectural context, if the system is applied to complex structures like skyscrapers, the tool can automatically check facade alignment against design plans. Here, tolerance levels might be set to ensure facade elements are placed within ±1 centimeter of their intended positions, reflecting both aesthetic and structural integrity. The automatic tool immediately highlights non-compliance, directing attention to areas needing correction.

[0045] In deploying these automatic tools, the seamless integration of CAD model data, AR visualization, and deviation detection algorithms provides a robust framework for real-time and highly accurate site modification verification. The approach not only expedites the verification process but also reduces reliance on manual inspection, improving both the safety and consistency of modification assessments across diverse project applications.

[0046] Automatic decision tools may be used independently, that is, the automatic tool evaluates the modification work and issues a verdict (conclusion). For example, modification work approved or a list of rejections to be corrected. Alternatively, the automatic decision tool may work in conjunction with a human inspector, the automatic tool's conclusion serving as a recommendation and guidance to the human inspector that is free to issue a final verdict.

[0047] Variations of this embodiment could include a dynamic interface allowing users to interact with the AR display. Users may adjust the transparency of the overlay, zoom into specific site areas, and annotate observations directly onto the AR display. Such capabilitiessupport a more detailed examination of potential issues, facilitating swift decision-making to address discrepancies.

[0048] In yet another scenario, where the site comprises complex architectural structures, like skyscrapers, the AR overlay enables precise checking of facade alignments and window placements against as-planned plans, which is crucial for aesthetic and functional compliance. The ability to detect even minor misalignments in the architectural facade becomes achievable and cost-saving in terms of timely interventions.

[0049] Additionally, the AR overlay may be employed in environments with restricted access, where traditional checks pose challenges. For example, modifications on an elevated section of a bridge could be inspected by overlaying the as-planned design onto a live drone feed, thus minimizing the need for cumbersome rigging or scaffolding. This approach not only simplifies the inspection process but also amplifies safety outcomes by reducing personnel exposure to high-risk conditions.

[0050] The use of AR combines digital fidelity with real-world application, providing a significant technical advantage in confirming site modifications during or post-construction phases. Through the AR overlay of the second CAD 3D model onto live drone feeds, the method significantly enhances the inspection process, ensuring design integrity and operational functionality of the completed site modifications.

[0051] In some embodiments, upon completion of the modification work, additional data is captured by the drone, focusing exclusively on areas identified for modification and / or for inspection. An area may not include modification work but still marked for inspection, for example, to verify that no damage has been sustained or other effects due to modification work on the site. This targeted data capture ensures efficiency by concentrating the drone's resources on regions pertinent to the modifications intended. The focused approach facilitates a more streamlined process by omitting unnecessary data acquisition from unaltered areas of the site, thereby optimizing the accuracy and resolution of the data related to the modified sections.

[0052] The present method involves a procedure for verifying modifications of a site using a drone system, focusing specifically on a process for detecting deviations of structural features between two CAD 3D models. This method facilitates the precise evaluation of modifications by employing drone-captured data to inform the comparative analysis of planned versus actual site states.

[0053] In practice, the process of detecting deviations involves the use of specialized software tools capable of performing geometric comparisons between the two models. These tools can evaluate differences in volumetric bounds, surface contours, and spatial relationships between model elements. In some instances, machine learning algorithms may enhance deviation detection by learning from historical modification data, thus offering a predictive capacity to better flag potential disparities.

[0054] Examples of practical application include:

[0055] In urban construction projects, where precise alignment of infrastructure elements such as walkways or utilities is critical, the method allows for verification that newly implemented components conform to the city's planned public works scheme. On roadway expansion projects, the technique is utilized to ascertain that the new lanes or road extensions follow design specifications for safety and functional integration with existing routes.ln the context of telecommunications infrastructure updates, such as those involving mobile network communication towers, this method ensures that new structural installations or adjustments comply with both engineer plans and regulatory height limits without interfering with service delivery.

[0056] Through this systematic comparison of CAD models, the method assists in ensuring adherence to design intentions, providing users with the assurance that modifications have been executed with fidelity to the original plans.

[0057] In accordance with the method for verifying modifications of a site using a drone system includes the step of issuing a report following the comparison of CAD 3D models. After determining and identifying deviations between the second, as-planned CAD 3D model, and the third, as-built CAD 3D model, a report is generated to convey the outcomes of the modification verification process. The report serves the function of either approving the modification work or providing detailed rejections based on detected discrepancies.

[0058] An example of this application is in the context of a construction project for an urban building. Once the comparison has been conducted on the superimposed parts of modified CAD 3D model of the drone's video feed, a thorough report is generated. This report includes sections outlining areas where the construction aligns accurately with the as-planned model and highlights areas where deviations were observed. For instance, if certain structural elements, such as window placements or wall alignments, do not conform to the planned modifications,the report will specify these discrepancies, allowing project managers to address any non- compliance with the design specifications.

[0059] Another illustrative scenario involves a telecommunications infrastructure project where modifications to existing network towers are being assessed. Upon completing the comparative analysis, a report provides an account of compliance with regulatory requirements. Should any deviations impact structural integrity or interfere with signal transmission, the report would highlight these issues, recommending corrective actions and ensuring that the modifications adhere to engineering and safety standards.

[0060] Furthermore, the report generation step involves synthesizing data and findings from the comparison into a digestible format for stakeholders. It may utilize charts, annotations on CAD models, and written summaries to effectively communicate the assessment results. This systematic generation of reports enables efficient decision-making processes, allowing stakeholders to take appropriate actions based on the reliable verification of site modifications.

[0061] The comparison-generated report, thus, not only documents compliance or deviations but also supports transparency and accountability within the project lifecycle, ensuring modifications meet the intended design specifications and regulatory standards.

[0062] In accordance with the method for verifying modifications of a site using a drone system includes a step wherein the second computer-aided design (CAD) 3D model, representing the as-planned alterations, is superimposed via Augmented Reality (AR) over a live video feed captured by the drone. This implementation enhances the visual confirmation process by providing an immediate, real-time overlay of the planned modifications directly onto the physical site as viewed through the drone's camera.

[0063] One embodiment of this approach involves deploying a drone equipped with a high- resolution AR-compatible camera to capture live video footage of the site post-modification. The second CAD 3D model is then digitally overlaid onto this live feed. This step allows users to visualize the planned modifications in situ, effectively blending the virtual elements with the real-world environment as perceived during the drone's flight. The overlay process assists in identifying potential discrepancies or alignment issues between the designed plans and the built environment.

[0064] For example, in a construction site scenario, upon hovering the drone over newly erected structures, the AR display might project the planned wall sections, roof lines, or other features onto the live video. This visual aid allows site managers and engineers to confirmwhether the constructed elements, such as beam placements or window openings, match the initial design specifications outlined in the as-planned CAD model.

[0065] In another example, for infrastructure projects like roadway expansions, the AR overlay assists in verifying that newly laid pavements or curbs are in conformity with the layout planned in the CAD model. The AR display can instantly highlight any deviations in alignment or measurement, enabling corrective actions to be taken swiftly to ensure compliance with design criteria.

[0066] Further embodiments may incorporate user interfaces that allow on-site project managers to interact with the augmented display. Through such interfaces, users can adjust the transparency of the overlay, annotate specific areas of interest, or compare various stages of work completion. This interactive capability facilitates a dynamic verification process, enabling stakeholders to make real-time decisions based on the contextual comparison of planned and actual outcomes.

[0067] The utilization of AR in this manner provides a powerful tool for immediate assessment and verification, leveraging the drone's capabilities to deliver a comprehensive visual analysis of site modifications. This approach enhances the accuracy and efficiency of the verification process by providing a clear, augmented perspective of the site, thereby promoting thorough evaluation and ensuring adherence to intended design specifications.

[0068] In some embodiments, the of step surveying the modified site is performed before all modification work is completed, utilizing the drone system to intermittently capture data during the modification process. This intermittent data capture enables the generation of superimposed parts of the as-planned CAD 3D model over a video feed from a drone surveying the site, at various modification stages, allowing stakeholders to track the progression of modification work against the as-planned CAD model.

[0069] In one example, during the modification of a construction site, the drone system is deployed to capture data at predefined intervals. This regular data acquisition allows for the creation of partial reports at each interval, providing insights into the alignment of ongoing work with design specifications. Consequently, project managers can identify deviations early, prompting immediate corrective actions.

[0070] In another embodiment concerning a mobile network communication tower, the drone system captures data before the final completion of modifications, focusing on stages where critical structural adjustments are undertaken. For instance, during the installation of additionalantennae or structural reinforcements, the captured data is used to generate interim status checks that compare the performed modifications with the as-planned requirements, facilitating verification of compliance with height restrictions, alignment, and load-bearing specifications.

[0071] Furthermore, the partial report generation supports risk management practices by highlighting potential issues, enabling timely intervention to prevent costly rework. The data captured during interim modification actively informs project planning, ensuring resource alignment and improved decision-making processes. This embodiment underscores the value of continuous oversight, demonstrating how proactive monitoring with partial reports can optimize modification outcomes and ensure alignment with project objectives before final completion.

[0072] Fig. 2 illustrates a schematic diagram of a system designed for the verification of site modifications using a drone-based system. The diagram presents multiple integrated components that facilitate the acquisition, processing, and analysis of site data to ensure the fidelity of modifications concerning the as-planned design.

[0073] The configuration features a drone equipped with sensors capable of capturing high- resolution imaging and geospatial data from the site. These sensors are instrumental in gathering accurate location and visual information for initial and subsequent assessments of the site modifications. The drone transmits the captured data to a processing module which generates a computer-aided design (CAD) 3D model of the existing site conditions.

[0074] A modification module is included to utilize computational design tools, allowing alteration of the initial CAD 3D model to reflect the intended modifications. The module takes into account desired changes such as the addition, removal, or alteration of site structures, producing an as-planned CAD model that represents the planned modifications.

[0075] In addition, the system incorporates a data capture module for post-modification surveying, where the drone, preferably similar in configuration, is employed to capture updated data from the modified site. This post-modification data collection is critical for generating an as-built CAD 3D model, providing a digital representation of the site following the completion of modification work.

[0076] An Augmented Reality (AR) module is configured to superimpose sections of the as- planned CAD model onto live video feeds captured by the drone, overlaying the planned modifications onto the physical site. This visual overlay facilitates direct comparison andinspection, enhancing the understanding of how well the executed modifications align with the design plans.

[0077] Moreover, the system integrates a decision module responsible for analyzing variations between the as-planned and as-built models, detecting deviations in dimensions, spatial alignments, and structural integrity. The decision module may utilize machine learning algorithms and automated tools to ensure comprehensive and accurate assessments.Alternatively, the analysis and approval or rejection can be performed by a human being, solely, or in conjunction with an automated tool.

[0078] Furthermore, this diagram depicts a user interface that interacts with the AR module's output, allowing stakeholders to receive real-time visual feedback on compliance with the as- planned specifications. By integrating these components, the system supports efficient site modification verification, aiding in identifying discrepancies and facilitating corrective actions if necessary.

[0079] In another aspect, the present invention relates to a system for verifying modifications of a site using a drone system comprising several distinct modules and components working in conjunction to ensure the fidelity of site modifications relative to planned specifications. The primary component of the system is a drone configured to capture initial data from the existing site. This drone is equipped with sensors capable of acquiring high-resolution images and geospatial data, forming a crucial input for creating an initial computer-aided design (CAD) 3D model, which accurately represents the site's pre-modification state.

[0080] The system includes a modification module, which is responsible for altering the initial CAD 3D model to integrate desired changes. Such changes are inputted through computational design automation tools, resulting in a second CAD 3D model that embodies the as-planned condition of the site. The modification module ensures that the alterations adhere to the intended structural and design objectives, capturing additions, removals, or modifications of site components as planned.

[0081] Upon completion of site modification work, a data capture module involves utilizing the same or a similar drone to survey the site anew. This step captures updated data necessary for constructing an as-built CAD model, reflecting the actual condition of the modified site. This post-modification data forms the basis for subsequent verification and analysis against the as- planned model.

[0082] For enhanced interaction and decision-making, a user interface is incorporated for visualizing the comparison outcomes. This interface may include overlay capabilities via Augmented Reality (AR), where the as-planned CAD 3D model is superimposed onto a live video feed from the drone. This AR feature aids in direct visualization of conformance and assists onsite personnel in identifying discrepancies during real-time drone surveys. The Augmented Reality (AR) module is incorporated into the system to enhance visualization during the verification process. This module functions by overlaying parts of the second CAD 3D model, representing areas that were to be modified, onto a video feed captured by the drone. The AR capability allows for real-time inspection and comparison of the as-built site with the as-planned design, providing immediate visual feedback and facilitating alignment verification.

[0083] The system also comprises a decision module, tasked with determining whether the modification work complies with the as-planned design criteria. This module may employ sophisticated analysis tools, such as machine learning algorithms, to detect deviations between the as-planned and as-built models. The decision module generates insights on dimensions, alignments, and structural integrity, ensuring a rigorous evaluation of modification fidelity.

[0084] Overall, the system for verifying modifications of a site using a drone system delivers a comprehensive framework for ensuring compliance with planned modifications, combining advanced data capture, computational modeling, AR visualization, and analytical tools to facilitate enhanced oversight and project management.

[0085] The system exhibits versatility in its application and can accommodate diverse project types, including mobile network communication towers. This adaptability ensures a comprehensive assessment of modifications within constraints specific to structural requirements in telecommunications infrastructure. For instance, ensuring the alignment and compliance of newly added antennae or structural supports is critical in maintaining regulated safety and operational standards.

[0086] In an alternative embodiment, the system may incorporate a monitoring unit designed to selectively capture data in regions of the site earmarked for modification. This focused capture approach maximizes resource efficiency and enhances the model's accuracy regarding targeted modification regions, thereby optimizing both time and computation resources.

[0087] Overall, the system facilitates rigorous verification of site modifications through a strategically integrated approach, combining aerial data collection, computational modeling, and real-time analysis tools. This integrated system supports effective oversight and assuresalignment with intended structural goals, contributing to improved project management and accountability.

[0088] In some embodiments the system utilizes machine learning algorithms to enhance the accuracy of the initial CAD 3D model generation. This is achieved by processing the raw data captured by the drone, which comprises high-resolution images and geospatial information. The integration of machine learning algorithms allows for intelligent analysis and interpretation of captured data, optimizing the initial model's fidelity in representing the pre-modification state of the site.

[0089] In one example, the machine learning algorithms employed are designed to identify and classify various structural components, such as walls, roofs, or other architectural elements, within the drone-captured imagery. By training on a dataset of pre-identified structures, these algorithms can automatically annotate the CAD model with accurate dimensions and spatial relationships, mitigating the need for extensive manual input.

[0090] Another example involves the application of machine learning algorithms to detect anomalies or inconsistencies in the captured data that could arise from environmental variables such as lighting or obstructions. The algorithms can dynamically adjust the processing parameters, ensuring that the generated initial CAD model accurately reflects the site's true conditions. This adaptability enhances the model's precision and supports more reliable subsequent modification planning.

[0091] Further, in scenarios where the site comprises complex topographical features, such as varying elevations or textured surfaces, machine learning algorithms are utilized to process the geospatial data effectively. These algorithms can discern subtle altitude changes or surface variations, incorporating them into the initial CAD model. By doing so, the algorithms contribute to a more detailed and comprehensive digital representation, facilitating precise modification work.

[0092] The use of machine learning within this context not only augments the speed and accuracy of initial model generation but also provides a framework for continuous improvement as more data becomes available. The system's capability to learn from previous model generation processes allows it to refine its algorithms, progressively enhancing performance and model accuracy.

[0093] Moreover, in an urban setting with buildings of diverse architectural styles, the machine learning algorithms can analyze stylistic nuances and elements specific to the local architecture.This capacity facilitates the accurate capture of design intricacies, ensuring that the initial CAD 3D model aligns with both structural realities and aesthetic expectations. Consequently, this precise model serves as a robust foundation for modifying and verifying site alterations, contributing to informed decision-making and efficient project execution.

Claims

Claims1. A method for verifying modifications of a site using a drone system, the method comprising: a. generating an initial computer-aided design (CAD) 3D model of an existing site;b. modifying the initial CAD 3D model to incorporate desired changes to the site, resulting in a second CAD model representing the as-planned condition of the to be modified site;c. after modification work is completed, surveying the site by a drone;d. superimposing in real-time via Augmented Reality (AR) parts of the second CAD 3D model representing areas to be inspected, on top of a video feed from the drone of step c.; and e. deciding whether the modification work has been executed according to plan.

2. The method of claim 1, wherein generating an initial CAD 3D model is done by using data captured by a drone.

3. The method of claim 1, wherein the data captured by the drone comprises aerial imaging and geospatial information.

4. The method of claim 3, wherein the geospatial information uses high accuracy positioning, such as Real Time Kinematic (RTK) positioning.

5. The method of claim 4, wherein the high accuracy positioning is accurate within at least 3 centimeters.

6. The method of claim 1, wherein the step of modifying the first CAD 3D model comprises the use of computational design automation tools for implementing the desired changes.

7. The method of claim 1, wherein the desired changes to the site comprise: adding a part or structure, removing a part or structure, modifying or repairing a part or structure, or any combination thereof.

8. The method of claim 1, wherein after modification work is completed, the drone only captures data around areas that were to be modified.

9. The method of claim 1, wherein deciding whether the modification work has been executed according to plan involves detecting deviations of structural features.

10. The method of claim 1, wherein after deciding whether the modification work has been executed according to plan, a report is issued either approving all modification work or providing a list of rejections.

11. The method of claim 1, wherein steps c to 3 are performed before all modification work is completed, in order to generate a partial report of modification work performed.

12. The method of claim 1, wherein superimposing via AR parts of the second CAD 3D model is performed in near real-time.

13. The method of claim 1, wherein deciding whether the modification work has been executed according to plan is performed by a machine.

14. The method of claim 1, wherein the site is a mobile network communication tower.

15. A system for verifying modifications of a site using a drone system, the system comprising: a. a drone configured to capture initial data from an existing site, used to generate an initial computer-aided design (CAD) 3D model;b. a modification module configured to alter the initial CAD 3D model to include desired changes, resulting in a second CAD 3D model representing the as-planned condition of the site;c. a data capture module for surveying the site with the same or similar drone after modification work is completed;d. an Augmented Reality (AR) module for overlaying in real-time parts of the second CAD 3D model representing areas that were to be modified onto a video feed from the drone; ande. a decision module for determining whether the modification work has been executed according to the as-planned design.

16. The system of claim 15, wherein the drone captures data comprising aerial imaging and geospatial information.

17. The system of claim 16, wherein the geospatial information utilizes high accuracy positioning, such as Real Time Kinematic (RTK) positioning systems.

18. The system of claim 17, wherein the high accuracy positioning systems achieve data accuracy within at least 3 centimeters.

19. The system of claim 15, wherein the modification module applies computational design automation tools for modifying the initial CAD 3D model to reflect desired changes.

20. The system of claim 15, wherein the desired changes to the site comprise: adding a part or structure, removing a part or structure, modifying or repairing a part or structure, or any combination thereof.

21. The system of claim 15, wherein, after modification work is completed, the drone captures data only around areas that were to be modified.

22. The system of claim 15, wherein the decision module involves detecting deviations of structural features to ascertain compliance with the planned modifications.

23. The system of claim 15, further comprising a reporting module configured to generate reports approving all modification work or providing a list of rejections based on detected discrepancies.

24. The system of claim 15, wherein the modules operate to enable the generation of a partial report of modification work performed before full completion.

25. The system of claim 15, wherein the AR module superimposes parts of the second CAD 3D model representing areas that were to be modified in near real-time.

26. The system of claim 15, wherein the decision module employs an automated analysis tool to determine whether the modification work has been executed according to plan.

27. The system of claim 15, wherein the site comprises a mobile network communication tower, and the system assesses modifications within constraints specific to such infrastructure requirements.

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