Computer aided rebar measurement and inspection system

Embodiments of the present invention generally relate to computer aided rebar measurement and inspection systems. In some embodiments, the system may include a data acquisition system configured to obtain fine-level rebar measurements, images or videos of rebar structures, a 3D point cloud model generation system configured to generate a 3D point cloud model representation of the rebar structure from information acquired by the data acquisition system, a rebar detection system configured to detect rebar within the 3D point cloud model generated or the rebar images or videos of the rebar structures, a rebar measurement system to measure features of the rebar and rebar structures detected by the rebar detection system, and a discrepancy detection system configured to compare the measured features of the rebar structures detected by the rebar detection system with a 3D Building Information Model (BIM) of the rebar structures, and determine any discrepancies between them.

FIELD

Embodiments of the present invention generally relate to computer aided rebar measurement and inspection systems.

BACKGROUND

A problem faced on building construction sites is a rigorous inspection needs to be done after each stage of construction. The impact of undetected errors is huge since the builder may need to replace parts or a whole building. Critical in this process is inspecting the rebar structure before concrete is poured over the rebars. Currently inspection rebar is done manually. This is a very slow and tedious process that must be performed to ascertain compliance of the rebar to design plans, building codes, building models, etc. As labor costs increase and finding skilled labor to perform inspections, and associated repairs/maintenance as required, it becomes prohibitively expensive to perform manual inspections, both in manpower costs and delays waiting for inspections to be completed.

In many cases, certain mistakes are too costly to fix after a certain point. Thus, construction/work cannot continue on certain structures until the inspection is completed. Therefore, it is necessary to ensure that various aspects of the construction are done according to plan/code. Furthermore, to inspect large scale structures and construction sites having thousands of rebar structures, it requires a lot of time and manpower to search the site for the rebar to be inspected, carry all the necessary drawings/plans, inspect the site, mark any inconsistencies, etc.

Thus, there is a need to replace tedious and slow manual work with system and methods that can assist in performing automated fine level inspections of rebar and comparisons to existing building plans.

SUMMARY

Embodiments of the present invention generally relate to computer aided rebar measurement and inspection systems. In some embodiments, a computer aided rebar measurement and inspection system includes a data acquisition system configured to obtain fine-level rebar measurements of a rebar structure and/or rebar images or videos of the rebar structures, a 3D point cloud model generation system configured to generate a 3D point cloud model representation of a rebar structure from the rebar measurements and/or rebar images/videos acquired by the data acquisition system, a rebar detection system configured to detect rebar within at least one of the 3D point cloud model generated or the rebar images or videos of the rebar structures acquired by the data acquisition system, a rebar measurement system to measure features of the rebar and rebar structures detected by the rebar detection system, and a discrepancy detection system configured to compare the measured features of the rebar structures detected by the rebar detection system with a 3D Building Information Model (BIM) of the rebar structures, and determine any discrepancies between them.

In some embodiments, a computer aided rebar measurement and inspection method may include acquiring fine level measurements and/or images of a rebar structure from a first set of high-resolution sensors configured to obtain mm level measurements; generating a 3D point cloud representation of the rebar structure using the fine level measurements and/or images of a rebar structure acquired; detecting rebars in the 3D point cloud representation and/or images of the rebar structure; determining measurements and information of said detected rebars; comparing a 3D building information model (BIM) of the rebar structure with the measurements and information determined about the detected rebars; and detecting discrepancies between the rebars detected and the BIM.

Other and further embodiments in accordance with the present principles are described below.

DETAILED DESCRIPTION

Embodiments of a computer aided rebar measurement and inspection system (“CARMIS”) are described herein. This disclosure describes inventive concepts with reference to specific examples. However, the intent is to cover all modifications, equivalents, and alternatives of the inventive concepts that are consistent with this disclosure. Numerous specific details are set forth, such as number of steps, types of cameras, positioning of the camera, types of images used, pitch measurements, BIM models, methodologies for rebar axis estimation display with differing visualization, etc., in order to provide a thorough understanding of the present design. It will be apparent, however, to one of ordinary skill in the art that the present design can be practiced without these specific details. Thus, the specific details set forth are merely exemplary. Also, the features implemented in one embodiment may be implemented in another embodiment where logically possible. The specific details can be varied from and still be contemplated to be within the spirit and scope of the present design. The CARMIS simplifies the inspection task in multiple ways including, but not limited to: 1) allowing a user to visually compare the constructed rebar structure to the plan (a building information model (BIM)); and 2) automatically measuring rebar properties (count, diameter, position, etc.), comparing to the BIM model, and generating a compliance report.

To accomplish this, embodiments of the present invention may: 1) detect, group and geo-locate rebars at each area; 2) make high-accuracy local measurements (millimeter-level); 3) compare constructed rebars to the BIM; 4) overly the BIM on an image of the scene to enable live comparison; and 5) provide an interface and visualization system for user interaction and display of results. The impact of such a system is that it simplifies the inspection process and will reduce the time and number of people needed for inspection. This translates to shorter construction time and reduced cost. As an added benefit, adopters would save time and resources using the invention.

Embodiments of the present invention can ascertain the compliance of the construction of rebar structure with respect to the BIM. To accomplish this, the system replaces tedious and slow manual work with an automatic system that, in at least some embodiments: acquires 3D measurements and images from the scene using a cameras and other sensors (e.g., such as a trinocular stereo camera system) to get mm-level local measurements of the rebar structure; detects rebars in 3D point clouds and/or images; measures rebar assembly (e.g., number, diameter, pitch, etc.) using rebar segmentation and diameter estimation from 3D data and image edges; detects rebar joints (welded or mechanically coupled); detects discrepancies between rebar construction and the BIM; refines alignment between the 3D point clouds measured to the 3D model provided by the BIM; and compares detected rebar with those in the BIM.

Various embodiments of the computer aided rebar measurement and inspection system (“CARMIS”) and methods and, more particularly, to methods and systems for using augmented reality and localization techniques to assist in performing fine level inspections and comparisons to 3D model of rebar structures, are now described in detail with respect toFIGS. 1-9.

FIG. 1depicts a high-level block diagram of a computer aided rebar measurement and inspection system (CARMIS)100in accordance with embodiments of the present principles. The CARMIS100includes a data acquisition system102, a data analysis system110, and a user interface180. In some embodiments, the data acquisition system102, data analysis system110, and user interface180may be disposed on a single device, such as hand-held sensor package200as described below with respect toFIG. 2. In other embodiments, the data acquisition system102, data analysis system110, and user interface180, or portions thereof, may be distributed across multiple devices and servers communicatively couple to each other as described below with respect toFIG. 2.

The data acquisition system102may be any device, or plurality of devices, able to obtain fine-level measurements104of a rebar structure and/or images or videos106of the rebar structures. In some embodiments, the rebar fine-level measurements104may include inertial measurement unit (IMU) data, depth information, and/or other information from barometer, altimeter, magnetometer, GPS, and other sensors/devices included in the data acquisition system102. In some embodiments, the images and videos106of the rebar structures may include one or more frames of stereo images obtained from stereo cameras.

For example, in some embodiments, the data acquisition system102may by a hand-held sensor package/device200as described in detail inFIG. 3. The hand-held sensor package/device200can obtain local relative measurements, and fine resolution images and videos, of the rebar structures that yields higher resolution by working with a narrower field of view. In some embodiments, the hand-held sensor package/device200used for local fine level relative measurements at a mm level of precision (e.g., about a 0.1 mm to about a 10 mm level precision, or in some embodiments, about a 1 mm to about a 5 mm level precision) includes a handheld device, such as a tablet302, that may include one or more cameras, a trinocular camera306a-c, a high-resolution range sensor, inertial measure unit (IMU)310, GPS, visualization capabilities, illuminator for lighting308, etc., as shown inFIG. 3. The handheld tablet302may also include handles314and a communication cable312(i.e., such as a USB cable) for transferring data. In some embodiments, data acquisition system102(e.g., the hand-held sensor package200) is configured to capture stereo images and information and IMU data by scanning a rebar section with tablet302. The tablet302may include three cameras able to capture images of at least 2 MP or more and having a 20-60-degree field of view (e.g., a 40-degree field of view). In some embodiments, the IMU data is used to generate a pose estimate. In other embodiments, feature correspondence or other methodologies are used to get the initial pose.

The data analysis system110includes 3D point cloud model generation system120that acquires a 3D representation of the scene (e.g., the rebar structure) from the rebar measurements104and rebar images/videos106from the data acquisition system102, and generates a 3D point cloud of the rebar structure being analyzed. In some embodiments, depth of the rebar structure can be acquired/determined by the 3D point cloud model generation system120through the use of stereo from a single image or video frame. In some embodiments, multiple images or video frames are used to cover a wider field of view and obtain a denser 3D point cloud. For example, as shown inFIGS. 4A and 4B, a stereo image of a rebar structure inFIG. 4Ais acquired by the data acquisition system102and provided to the 3D point cloud model generation system120. The 3D point cloud model generation system120then generates a 3D point cloud model of the rebar structure inFIG. 4B. In some embodiments, the 3D point cloud model generation system120processes the measurements104of a rebar structure and/or images and videos106of the rebar structures (e.g., trinocular stereo data and IMU data received from data acquisition system102) using at least one of dense stereo point cloud integration techniques and/or bundle adjustment techniques to compute integrated 3D points of a scanned area of the rebar structure. Bundle adjustment is run to refine the camera poses and 3D point locations corresponding to each rebar in the rebar structure. The Bundle adjustment algorithms use the fact that the relative motion between the multiple stereo cameras of the data acquisition unit is constant over time and it is known from calibration to fix the scale of the 3D reconstruction. The 3D point cloud model generation system120first runs visual navigation on the input video sequence to obtain initial pose estimates for each image. Based on these poses a subset of images (key-frames) is selected for 3D recovery. Next, feature tracks are generated across multiple frames. The 3D point cloud model generation system120then uses the refined poses to integrate points generated from dense disparity maps. Finally, the 3D point clouds from each stereo pair are aggregated to generate a dense point cloud for the inspected rebar structure.

The data analysis system110further includes a rebar detection system130to detect rebars. The rebar detection system130uses one or more of many different subsystems, types of information, and algorithmic techniques to accurately detect the rebar in the structures being analyzed as described below in further detail.

In some embodiments, the rebar detection system130detect rebars from the integrated 3D point cloud by refining the point cloud to remove outliers (also referred to as pruning or filtering the point cloud). The refined 3D point cloud after rebar detection is depicted inFIG. 5. As shown inFIG. 5, the rebar in the rebar structure can clearly be recognized. After the rebar detection system130generates the refined 3D point cloud, the cleaned 3D point cloud is represented in a 2D histogram to facilitate rebar detection. In some embodiments, the rebar detection system130detects peaks602in the histogram of projected 3D point clouds on the x-y, x-z, and y-z planes perpendicular to rebars along the horizontal and vertical direction as shown inFIG. 6. The diameter can be roughly estimated from the size of peak areas602. The diameter is refined using multiple frames using edge information (e.g., from images/videos106)—this offers increased accuracy and precision of the estimates. As shown inFIG. 6, the peaks602are a count (i.e., histogram) of the data points collected for each rebar—i.e., the rebar ends show up as peaks because many points land in the same location and that value there gets higher. That's used to both localize the bars and also filter out the noise because the noise (i.e., the grey area around the peaks).

In some embodiments, the rebar detection system130detects rebars using the stereo rebar images/video106and/or rebar measurements104. The rebar detection system130may detect rebar sets in frame of stereo images of roughly parallel bars using features such as rebar with similar diameter, rebar forming a plane, and possibly regularly spaced rebar as shown inFIG. 7. The rebar detection system130may use techniques such as vanishing point detection (i.e., geometric notion where parallel lines meet at a point and vanish), non-maximum suppression or other heuristic for rebar detection, or for detecting sets of rebar. Non-maximum suppression is a technique where the system identifies/detects some rebar—and therefore the system knows that the bars have some spacing there. Thus, if the system detecting a rebar, then it can assume that around that location it doesn't expect to see any more rebar, and looks a little further away from the detected rebar to detect other rebar.

One of the main challenges for the rebar detection system130is the precision of edge detection of the rebar. Precision/recall of boundary detection affects rebar detection and edge localization affects diameter estimation. As noted above, about a 0.1 mm to about a 10 mm level precision, or in some embodiments, about a 1 mm to about a 5 mm level precision, is achieved. The user creates a precisely annotated dataset by manually annotating each rebar's image boundary and extent (segments) as shown inFIG. 8. Then the 3D geometry can be recovered from annotations. The annotations are then turned into an occlusion mask to train edge detection using model training systems such as the structured random forest. The annotations are saved/stored as coordinates (locations within the images) for all the edges and the diameters.

An appearance detector can also be trained for rebar texture detection. Some exemplary model/training techniques that can be used include histograms of oriented gradients (HOG), Support Vector Machines (SVM), conditional random field (CRF) which is a kind of discriminative model for sequential data, pix2pix, CNN, and the like. The 3D geometry is recovered and physical deviations from model and assumptions (BIM, rebar alignment) can be analyzed to determine the rebar texture. Evaluation can be done use boundary detection PR curves, rebar detection PR curves, and radius/localization error distributions.

One of the goals of the rebar detection system130includes the ability to detect the texture appearance of rebars of different types, at different depths. The periodic pattern of the texture can be detected using pixel wavelength which depends on bar type and distance to camera. For example, Gabor wavelets can be used to detect localized pixel wavelength. Correlation can then be used for pattern matching. As an example, rebar texture can be matched: (1) along length of given rebar; (2) along rebars of similar orientation/type.

As discussed above, multi-frame or single time instant stereo images can be used to generate the 3D point cloud by the 3D point cloud model generation system120. Similarly, the rebar detection system130can use multi-frame or single time instant stereo images to detect rebar. This method has the advantages that it can incorporate the range from stereo to reduce the number of rebar hypotheses that need to be verified, and combine horizontal and vertical stereo to get good range estimates on all rebars (vertical and horizontal).

The rebar detection system130also performs a refined rebar axis estimation to determine the axis of the rebar structure. Specifically, the rebar detection system130uses rebar segmentation to compute a bounding box around each rebar segment. Then an axis estimate is refined for each rebar and the estimated axis902is overlaid on the 3D point cloud model as shown inFIG. 9. This allows for (1) estimation of the angle between rebar segments and a rebar segment and main axes, and (2) more precise pitch measurements, for bars that are not aligned with one of the axes.

The data analysis system110further includes a rebar measurement system140to measure features of the rebars and rebar structures detected by the rebar detection system130(e.g., measuring the number, diameter, pitch, etc. of the rebar and rebar structures). The rebar measurement system140includes one or more systems and employs various techniques for performing these measurements. For example, in some embodiments, the rebar measurement system140includes a 3D point cloud diameter estimation system142and/or an edge-based diameter estimation system144, each providing different ways to measure rebar diameter. Each system has its advantages. The 3D point cloud diameter estimation system142and an edge-based diameter estimation system144are also able to measure pitch and number of rebar, and is not limited to measuring rebar diameters.

For example, edge-based diameter estimation system144does not need the dense 3D point cloud, only camera poses and calibration to perform edge detection in order to estimate rebar diameters. Meanwhile, the 3D point cloud diameter estimation system142can estimate diameters faster since does not use the computationally expensive edge detection techniques/models. The 3D point cloud diameter estimation system142also benefits from stereo disparity maps at full resolution.

In some embodiments, the edge-based diameter estimation system144may use the precisely annotated dataset annotating each rebar's image boundary and extent (segments) as shown inFIG. 8in order to determine rebar measurements (e.g., measuring the number, diameter, pitch, etc. of the rebar and rebar structures). In some embodiments, it does so by integrating diameter measurement in the system and/or using a predefined lookup table to convert from diameter estimates to labels as shown inFIG. 10(e.g., rebar label/designation D29 has a certain diameter and pitch). Specifically, rebar comes in twelve or so possible diameters. The edge-based diameter estimation system144adjusts/verifies that the min and max values used for each “D” label matches the actual diameter of the bar.

In some embodiments, diameter estimation is achieved by fitting a circle to the 3D points projected on a plane perpendicular to the rebar axis. In some embodiments, both the 3D point cloud diameter estimation system142and the edge-based diameter estimation system144approaches are fused to provide more accurate and faster rebar measurements. In other embodiments, for the 3D point cloud approach, only rebar points from the 3D point cloud acquired from cameras close to that rebar are used to improve estimation. In further embodiments, given a rebar cluster, rather than estimating a diameter value and associating that value with discrete rebar designations “D10”-“D57”, if two diameter estimation calculations have similar support, report both with a lower confidence value.

In some embodiments, the data analysis system110further includes a rebar joint detection system150to detect joints (e.g., pressure welded joints of two rebar together or mechanically coupled rebar joints). The rebar joint detection system150detects rebar joints by (1) locating all intersections of a rebar with other rebar, (2) section the remaining portion of the rebar (i.e., other than the intersections) with a plane and compute an envelope of all the measured/detected points from the 3D point cloud, and (3) look for “bumps” in the profile of the envelope computing (i.e., portions where the thickness of the rebar point cloud increases). The bumps determined may be compared to a template of a rebar weld profile, or a mechanically coupled rebar joint profiled, to determine if the bump is actually a rebar welded joint or mechanically couple rebar joint.

In some embodiments, rebar joint detection system150uses multiple diameter estimate profiles along the rebar generated by the rebar measurement system140to support rebar joint detection. In some embodiments, the system can detect rebar joint and estimate the angle between the two joined rebar segments. Additionally, the system can consider point density to further reduce the number of false alarms. For example,FIG. 11depicts images of rebar structure including actual rebar joints1102.FIG. 12depicts a 3D point cloud of the images of rebar structure shown inFIG. 11with the rebar joints1202automatically detected by the rebar joint detection system150.

The data analysis system further includes a discrepancy detection system160that is configured to compare the measured features of the rebar and rebar structures detected by the rebar detection system140and/or joint detection system150with the 3D Building Information Model (BIM)170. The discrepancy detection system160performs the comparison with the BIM and determines any discrepancies between the measurements and information obtained about the rebar structure and the BIM of the rebar. The discrepancies are determined and compiled, and a compliance report is generated including those discrepancies or information/summary about the discrepancies. The discrepancies determined between the measurements and information obtained about the rebar structure and the BIM of the rebar structure may include differences in the number of rebars measured versus the number of rebars in the model, differences in the diameter/thickness of the rebar, pitch between each rebar, tensile markings on the rebar, angles of the rebar, etc. The compliance report may include, for example, what was measured and where the discrepancies are. In some embodiments, the measured data is compared to the model data, and values that exceed a predetermined threshold (e.g., 0.1-25% of expected value) may be flagged as a discrepancy and included in the compliance report. In some embodiments, if a measured value exceeds a predefined threshold, the element in error would be repaired/corrected and re-inspected. In some embodiments, the discrepancies may be visually detected by displaying the BIM170, or portion thereof, overlaid on the 3D point cloud model generated as shown inFIG. 13D.

In some embodiments, the discrepancy detection system160may first generate a point cloud model from the BIM170as shown inFIG. 13A, or may otherwise be provided with a point cloud model from the BIM170. The discrepancy detection system160will then obtain the 3D point cloud model generated by the 3D point cloud model generation system120as shown inFIG. 13B. The discrepancy detection system160will then crop a portion of the point cloud model of the BIM170corresponding to the 3D point cloud model generated by the 3D point cloud model generation system120as shown inFIG. 13C. The discrepancy detection system160will then refine the alignment of the 3D Point cloud of the scene to the BIM and show the discrepancies between scene and BIM as shown inFIG. 13D. InFIG. 13D, the image shows difference between the BIM with four main rebars and the 3D Point cloud of the scene with three bars.

In some embodiments, the CARMIS100includes a user interface180that includes a user interface control system182and data visualization system184. In one embodiment, the CARMIS100utilizes the user interface control system182to provide a user of the system an intuitive and easy to use interface to train, control, and calibrate the system.

The user interface control system182includes interactive buttons such as “Start Nav,” “Reset Pose,” “Auto Select,” Save,” “Start Capture,” “Process,” “Store Results,” “Color Detection,” “3D Measurements,” “Rebar Detection,” “Main Rebar,” “Hoop Rebar,” “BIM Comparison,” and “Joints,” as shown inFIG. 14A. The “Start Nav” button initializes the sensors and starts the navigation systems and modules. When the initialization process is complete, the user will see live video in the main GUI window of the display. Next step is to initialize the tablet sensor pose in the global (BIM) coordinate frame. To do this, the user selects the “Reset Pose” object. In some embodiments, the following selectable objects may be made available to the user by the user interface control system182as shown in Table 1:

TABLE 1UI Selectable ObjectsStepProcessButton active when reads3D RecoveryGenerate a dense 3D point cloud from theDistance MeasurementssequenceRebar DetectionInitial clustering of the point cloud to get subsets ofRebar Detectionthe 3D point cloud associated with each rebarhypothesisRebar refinementRefine axis and diameter estimates for rebarMain rebar, Hoop rebarhypotheses along one directionBIM ComparisonAlign the point cloud to the BIM model for theBIM Comparisoninspected element and compare detection resultswith the modelJointsDetect joints for bars in a given directionJoints

For example, as shown inFIG. 14B, if the user selects the “Rebar Detection” object on the user interface180, the CARMIS100will begin detecting rebar from the images and data acquired from the data acquisition unit102. Furthermore, as shown inFIG. 14C, information about the rebar detected is displayed and can be edited, annotated, measured (with virtual ruler), etc. The data visualization system184can include a display screen to output what the camera is currently capturing or a comparison image of BIM vs. calculated values. The display screen can further include an overlay option to overlay useful information such as current measurements calculated by the system, a ruler to make rough measurements as shown inFIG. 14C, and a whiteboard to with structural information.

As discussed above, and as shown inFIG. 2, the data acquisition system102, data analysis system110, and user interface180may be disposed on a single device, such as hand-held sensor package200. In other embodiments, the data acquisition system102, data analysis system110, and user interface180, or portions thereof, may be distributed across multiple devices, such as hand-held sensor package200, body worn sensor package210, and servers250, communicatively coupled to each other via network220.

The networks/cloud220comprise one or more communication systems that connect computers by wire, cable, fiber optic and/or wireless link facilitated by various types of well-known network elements, such as hubs, switches, routers, and the like. The networks220may include an Internet Protocol (IP) network or other packet-based communication networks, and may employ various well-known protocols to communicate information amongst the network resources. In some embodiments, hand-held sensor package200can communicate directly with body worn sensor package210, and server250through WIFI, BLUETOOTH, or any other wireless or wired communication protocols.

In some embodiments, the hand-held sensor package200may comprise a Central Processing Unit (CPU)202, support systems204, display206, memory208, 3D point cloud model generation system120, rebar detection system130, rebar measurement system140, rebar joint detection system150, discrepancy detection system160, BIM170, user interface180including user interface control system182and data visualization system184.

In some embodiments, the server250may comprise a Central Processing Unit (CPU)202, support systems204, display206, memory208, 3D point cloud model generation system120, rebar detection system130, rebar measurement system140, rebar joint detection system150, discrepancy detection system160, BIM170, user interface180including user interface control system182and data visualization system184.

In some embodiments, the body worn sensor package210may comprise a Central Processing Unit (CPU)202, support systems204, display206, memory208, geolocation tracking system212, BIM170, user interface180including user interface control system182and data visualization system184.

In some embodiments, a handshaking process is performed between elements of the hand-held sensor package200and the body worn sensor package210to align information between the two systems. Specifically, in some embodiments, the body worn sensor package210including helmet mounted cameras, sensors, and AR display may handshake with the hand-held sensor package200including a tablet to align a pose captured by the hand-held sensor package200with the pose captured by the body worn sensor package210. For example, the pose captured by the body worn sensor package210or hand-held sensor package200may be a six (6) degrees of freedom (6DOF) pose. This is achieved by sending a number of salient features (image feature descriptors and the corresponding 3D points) from the body worn sensor package210to the hand-held sensor package200. The hand-held sensor package200performs a 3D-2D matching based on the features received (and the matched image features in the tablet image to compute the 6DOF pose transformation (rotation and translation) between the tablet camera and the helmet camera. This transformation is then used to align the pose of the second sensor package with the pose of the first sensor package. This handshake procedure is initiated by the user (e.g., by pressing a button on the tablet or associated with the first sensor package) before recording a sequence for local inspection, to ensure that the second sensor poses are aligned to the global reference frame.

The CPU202may comprise one or more commercially available microprocessors, microcontrollers, FPGA, etc. that facilitate data processing and storage. The various support systems204facilitate the operation of the CPU202and include one or more clock circuits, power supplies, cache, input/output devices and circuits, and the like. In some embodiments, the support systems204include the data acquisition cameras and sensors to produce rebar measurements104and rebar images/videos106such as described herein with respect toFIG. 3. The input/output devices of support systems204may further include audio input and output (e.g., commands or instructions for repairs where discrepancies are found). The memory208comprises at least one of Read Only Memory (ROM), Random Access Memory (RAM), disk drive storage, optical storage, removable storage and/or the like.

FIG. 15depicts a flow diagram of a computer aided rebar measurement and inspection method1500for inspection, error analysis and comparison of rebar structures in accordance with a general embodiment of the present principles. The method1500starts at1502and proceeds to1504where fine level measurements and/or images of a rebar structure are acquired from a first set of high-resolution sensors configured to obtain mm level measurements. As noted above, about a 0.1 mm to about a 10 mm level precision, or in some embodiments, about a 1 mm to about a 5 mm level precision, of measurements are obtained. At1506, a 3D point cloud representation of the rebar structure is generated using the fine level measurements and/or images of a rebar structure acquired. The method proceeds to1508where rebars in the 3D point cloud representation and/or images of the rebar structure are detected. At1510, measurements and information of said detected rebars are determined. At1512, a 3D building information model (BIM) of the rebar structure are compared with the measurements and information determined about the detected rebars. The method proceeds to1514where discrepancies are detected between the rebar and the BIM. In some embodiments, detecting the discrepancies further includes generating a compliance report including the discrepancies determined. The method ends at1516.

FIG. 16depicts a computer system1600that can be utilized in various embodiments of the present invention to implement the computer and/or the display, according to one or more embodiments.

Various embodiments of computer aided rebar measurement and inspection, as described herein, may be executed on one or more computer systems, which may interact with various other devices. One such computer system is computer system1600illustrated byFIG. 16, which may in various embodiments implement any of the elements or functionality illustrated inFIGS. 1-15. In various embodiments, computer system1600may be configured to implement methods described above. The computer system1600may be used to implement any other system, device, element, functionality or method of the above-described embodiments. In the illustrated embodiments, computer system1600may be configured to implement the method1500as processor-executable executable program instructions1622(e.g., program instructions executable by processor(s)1610) in various embodiments.

In the illustrated embodiment, computer system1600includes one or more processors1610a-1610ncoupled to a system memory1620via an input/output (I/O) interface1630. Computer system1600further includes a network interface1640coupled to I/O interface1630, and one or more input/output devices1650, such as cursor control device1660, keyboard1670, and display(s)1680. In various embodiments, any of the components may be utilized by the system to receive user input described above. In various embodiments, a user interface may be generated and displayed on display1680. In some cases, it is contemplated that embodiments may be implemented using a single instance of computer system1600, while in other embodiments multiple such systems, or multiple nodes making up computer system1600, may be configured to host different portions or instances of various embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer system1600that are distinct from those nodes implementing other elements. In another example, multiple nodes may implement computer system1600in a distributed manner.

In various embodiments, computer system1600may be a uniprocessor system including one processor1610, or a multiprocessor system including several processors1610(e.g., two, four, eight, or another suitable number). Processors1610may be any suitable processor capable of executing instructions. For example, in various embodiments processors1610may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs). In multiprocessor systems, each of processors1610may commonly, but not necessarily, implement the same ISA.

System memory1620may be configured to store program instructions1622and/or data1632accessible by processor1610. In various embodiments, system memory1620may be implemented using any suitable memory technology, such as static random-access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing any of the elements of the embodiments described above may be stored within system memory1620. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory1620or computer system1600.

In one embodiment, I/O interface1630may be configured to coordinate I/O traffic between processor1610, system memory1620, and any peripheral devices in the device, including network interface1640or other peripheral interfaces, such as input/output devices1650. In some embodiments, I/O interface1630may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory1620) into a format suitable for use by another component (e.g., processor1610). In some embodiments, I/O interface1630may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface1630may be split into two or more separate components, such as a north bridge and a south bridge, for example. Also, in some embodiments some or all of the functionality of I/O interface1630, such as an interface to system memory1620, may be incorporated directly into processor1610.

Network interface1640may be configured to allow data to be exchanged between computer system1600and other devices attached to a network (e.g., network1690), such as one or more external systems or between nodes of computer system1600. In various embodiments, network1690may include one or more networks including but not limited to Local Area Networks (LANs) (e.g., an Ethernet or corporate network), Wide Area Networks (WANs) (e.g., the Internet), wireless data networks, some other electronic data network, or some combination thereof. In various embodiments, network interface1640may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via digital fiber communications networks; via storage area networks such as Fiber Channel SANs, or via any other suitable type of network and/or protocol.

Input/output devices1650may, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or accessing data by one or more computer systems1600. Multiple input/output devices1650may be present in computer system1600or may be distributed on various nodes of computer system1600. In some embodiments, similar input/output devices may be separate from computer system1600and may interact with one or more nodes of computer system1600through a wired or wireless connection, such as over network interface1640.

In some embodiments, the illustrated computer system may implement any of the operations and methods described above, such as the methods illustrated by the flowcharts ofFIG. 15. In other embodiments, different elements and data may be included.

In the foregoing description, numerous specific details, examples, and scenarios are set forth in order to provide a more thorough understanding of the present disclosure. It will be appreciated, however, that embodiments of the disclosure may be practiced without such specific details. Further, such examples and scenarios are provided for illustration, and are not intended to limit the disclosure in any way. Those of ordinary skill in the art, with the included descriptions, should be able to implement appropriate functionality without undue experimentation.

Embodiments in accordance with the disclosure may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device or a “virtual machine” running on one or more computing devices). For example, a machine-readable medium may include any suitable form of volatile or non-volatile memory.