علی حسینی نوه احمد آبادیان

The combination of laser range finder and camera has proven to be very useful in the fields of robotics, mapping, and autonomous vehicles for producing maps and capturing information about the color and texture of objects. In order to fuse data from these two sensors, they need to be calibrated with high accuracy, meaning that their translation vector and rotation matrix with respect to each other must be determined. This paper proposes a method for calibrating a 2D lidar with a camera using the same 3D test fields commonly used in photogrammetric tasks such as camera calibration. In the proposed method, a point-on-plane and an additional constraint are used to obtain the calibration parameters, which is similar to using a chessboard pattern in previous research. To implement and evaluate the proposed method, a system consisting of a 2D laser range finder, a stereo camera, and a servo motor was designed and built. This method was compared with one of the most accurate new calibration methods which used a photogrammetric test field and ping-pong balls. The advantage of the proposed method over this method is the lack of the need for ping-pong balls and the selection of the corresponding points in the point cloud, which is a manual and time-consuming process. Additionally, the proposed method was compared with the method used by Jia fen et al., which uses a pyramid structure created in the corner of a room for calibration. Finally, the calibration parameters of laser range finder with respect to the camera were calculated using the proposed method, the calibration method using a cloud of 3D points, and the J. Fan et al. method. The RMSE of the check points for these three methods was 24.14, 40.12, and 94.15 millimeters, respectively.

The combination of 2D laser rangefinder(LRF) and camera has many application in robotics, mapping,self-driving vehicles to map and capture  color and texture information of objects. to fuse LRF and camera’s data, these  sensors must be carefully calibrated related to each other. Extrinsic calibration between a LRF and camera is often performed by common features. Therefore extrinsic calibration between LRF and camera is necessary process.  Extrinsic calibration between a Camera and a LRF  is often performed by common features in data captred by two sensors. In this research, three difrent methods for extrinsic calibration between a LRF and a camera using photogrammetric control field and ping pong balls are presented. In calibration process using 3D and 2D point clouds,ping pong balls are used as common targets that can be identified in the camera and LRF data. In calibration using the 3D point clouds, the calibration parameters are calculated using the point clouds generated from the test field at the main station and performing the bundel adjusment using a set of images taken from the control field. In this method, a 3D point cloud is obtained from combination of 2D LRF and servo motor data. The 2D LRF  is connected to the servo motor by a gimbal so that the extension of the servo motor shaft pass through the center of the LRF. The LRF rotation is measured by the encoder in the servo motor, and by rotating the LRF, all 2D scans at a station can be registered relative to each other. On the other hand, through images taken from the control field and bundel adjusment technoque, the position of the balls in the photogrammetric model is obtained. The second method is similar to the first method,except that a 2D point cloud is used instead of a 3D point cloud. In the first and second methods, the coordinates of the centers of ping pong balls are available in two different coordinate systems, so the relationship between the two coordinate systems with the 3D conformal equation is obtained. In the third method, through the pyramid created in the corner of the room and solving the perspective-three-point problem,  the 2D LRF calibration parameters at the main station relative to the control field are calculated with just one scan. The calibration parameters of camera in the main station relative to the control field are calculated using bundel adjusment and 3D conformal equations. Finally, the calibration parameters of the LRF relative to the camera are obtained. According to the RMSE calculated for checkpoints, calibration using 3D point cloud,calibaration using room corner and calibration using 2D point cloud were the most accurate, respectively.

On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds using a trained deep learning network is proposed, which includes steps: 1) Preparation and normalization of point cloud data, 2) completion of the point cloud by learned networks; 3) reversion of the completed point cloud to real-world coordinates and, 4) integration with the existing original point cloud and preparation of the digital surface model and generation of true orthophotos.

In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the trained SnowflakeNet network is used to complete the incomplete roof points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were introduced as inputs to the network to complete. Points related to edge points were extracted for each roof and added to the existing point cloud after increasing the density and returning to the actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.

The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. In the proposed method, these point clouds are complemented by the deep learning method, and quantitative and qualitative comparisons show better results in reducing distortion in most of the buildings tested. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and integrated form of the roof of each building instead of multiple line segments and considering the outermost edges of the buildings.

In this study, a new method was introduced to improve the quality of true orthophoto edges by using a deep learning network to complete the point cloud, which was tested on several building images and compared with the results of previous methods. In this study, in addition to the fact that, for the first time, a deep learning network was used to improve point clouds to produce orthophotos, Compared to the previous method, the amount of distortion on the selected edge of four buildings has been significantly reduced and the success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy. Despite the reduction of distortion on the selected structural edge using the proposed method, this value is increasing in curved areas as well as the corners of the roofs due to the type of network training and network output error. However, this can be reduced by improving the structure of the deep learning network and increasing the training data to a variety of roof modes with curved walls.

Due to the complexity of frame processing used for positioning and mapping in visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms, key-frame selection methods have been introduced to improve the performance and decrease the number of frames required for processing while maintaining accuracy and robustness of the algorithms. Selected key-frames in these methods make a very good representation of all available frames. The current key-frame selection methods rely on heuristic thresholds in their selection procedure. Researchers have used several datasets to find optimum values for these thresholds through trial and error. In fact, proposed methods may not work as expected with a new dataset due to changes occurring in the sensor, environment and the platform.

The present study has proposed an improved geometric and photogrammetric key-frame selection method built upon ORB-SLAM3, as the state of the art visual SLAM algorithm. The proposed Photogrammetric Key-frame Selection (PKS) algorithm has replaced inflexible heuristic thresholds with photogrammetric principles and thus guaranteed the robustness of the algorithm and the quality of the point cloud obtained from the key-frames. First, an adaptive threshold decides the allowable number of points whose line of sight zone has changed on a four-zone cone built upon each point. Increased number of points whose line of sight zone has changed means increased changes and displacements of the frame and thus, increased need for a new key-frame. Then, a 3*3 grid was formed in each frame and the number of points with a more than 30-degree change in line of sight angle (effective points) in each cell were counted. Later, the Equilibrium of Center Of Gravity (ECOG) criterion decides whether the distribution of points is appropriate using the center of gravity of the points inside the frame. Appropriate distribution of effective points within the frame shows a high geometric strength and thus will improve the strength of key-frames network. IMU sensor  is not dependent on the position of the frames and the camera sensor. Thus, it independently obtains the key-frame in case significant changes occur in acceleration. The threshold value of acceleration has been experimentally considered equal to 1 meter per square second, which entirely depends on the type of robot. For ground robots with slower moving speeds, this threshold must be reset.

The present study has employed data collected by the European Robotics Challenge (EuRoC) flying robot containing the information collected by the synchronized camera and IMU information, as well as the ground truth data such as the robot trajectory and point cloud formed by the laser scanner. To evaluate the proposed method, extensive experiments have been implemented on the EuRoC dataset in mono-inertial and stereo-inertial modes. Then, trajectory of each algorithm was compared with the reference trajectory and point clouds formed by the key-frames were also compared. Apart from these qualitative evaluations, absolute trajectory error (ATE) obtained from running the PKS and ORB-SLAM3 algorithm 10 times were also compared quantitatively and finally, the error histogram was used to evaluate the point clouds. The processing time of each algorithm was also evaluated for each EuRoC dataset sequence. Results indicated that the proposed algorithm has improved ORB-SLAM3 accuracy in stereo-inertial by 18.1% and in the mono-inertial mode by 20.4% producing a more complete and accurate point cloud and thus, extracting more details from the environment. Furthermore, despite higher density of the point cloud, the error histogram has not changed significantly and fewer errors were observed in the ORB-SLAM3 algorithm.

Findings indicated that the PKS method has succeeded in extracting key-frames using photogrammetric and geometric principles. Apart from improving the positioning accuracy of the robot, the method has produced a much more complete and dense point cloud as compared to the ORB-SLAM3 algorithm. Also, dependency of the PKS method on the environment conditions and the type of system used (stereo camera or mono camera) was greatly reduced. Future studies can expand our key-frame selection method to include fisheye cameras or visual-only systems. More geometric conditions (near and far point condition and the vertex angle in the triangle formed by the points in the current frame, the camera and the corresponding points in the last key-frame) can also be added to the key-frame selection method.

Underground infrastructure such as electricity, gas, telecommunications, water and sewage are managed by different organizations. Since most projects in these organizations require drilling,and imprecise excavations will endanger infrastructure and result in extensive financial and physical losses, drilling projects require having accurate information about the infrastructure status. However, reaching accurate position of facilities such as pipes and cables is difficult due to their being concealed underground.Nowadays, ubiquitous computing and new developments in Geospatial Information Systems (GIS) can be an appropriate solution to such problems. This new generation of GIS is called the Ubiquitous Geospatial Information System (UBGIS). New technologies such as Augmented Reality (AR) can visualize this infrastructure on platforms like smart phones or tablets. Such technologies show spatial and descriptive attributes of these utilities more interactively, and thus can be applied as a modern solution for this problem. One of the major features of AR is identifying and locating real-world objects with respect to the person’s head or a camera. To have an accurate Augmented Reality, the position and orientation (pose) of the camera should be estimated with high accuracy. Therefore, exterior orientation parameters of the camera are required for AR and tracking. Different methods are used to calculate these exterior orientation parameters. One of the most common methods applies different sensors,such as Global Positioning System (GPS) and Inertial Measuring Unit (IMU),embedded in smart phones or tablets to calculate these parameters. These sensors include accelerometers, gyroscopes, magnetic sensors and compasses. Althoughsimple and fast, this method is not suitable for accurate cases, because sensors of mobile phones or tabletscannot provide such high accuracy. Vision-based (sometimes called image-based) method is another way of estimating exterior orientation parameters. In this method, fixed or dynamic images are used to determine the position and orientation of camera. The method is more complex and slower, but more accurate than the first one.

Regarding previously mentioned issues, the present article aims to visualize underground infrastructure using both sensor-based and vision-based approaches of Augmented Reality. Since the sensors embedded in a mobile phone or tablet do not provide such an accuracy (an accuracy of a few centimeters considering diameter of pipes and width of streets and pavements), a novel vision-based approach is proposed. In this method, image-based techniques and special kinds of targets, known as coded targets, are used to estimate camera’s position and orientation along with space resection method. In photogrammetry,space resection involves determining the spatial position and orientation of an image based on thesize of ground control points appearing on the image. Since space resection is a nonlinear problem, existing methods involve linearization of the collinearity condition and the use of an iterative process to determine the final solution using the least squares method. The process also requires determination of the initial approximate values of the unknown parameters, some of which must be estimated using another least squares solution. In order to obtain suitable initial values for space resection procedure, data received from GPS, accelerometers, and magnetic sensors are used and a low-pass filter is applied to reduce noise and increase precision. Then, due to improved camera pose parameters, the resulting virtual model is overlaid at its correct real worldplanimetriclocation. The planimetric coordinates are shown graphically on the ground and the Z coordinate (depth) is presented as a descriptive parameter.

Both proposed methods were implemented and tested in an Android Operating System. Camera pose parameters were estimated and the virtual modelwas overlaid at its correct real world planimetric location and shown on camera. Then, the results were compared and evaluatedusingthe well-known photogrammetry software, Agisoft, with the aim of modelling and precise measuring based on basic photogrammetry and machine vision. For sensor-based method, mean accuracy of the position parameters equals 4.2908±3.951 meters and mean accuracy of orientation parameters equals 6.1796±1.478 degrees,whilein vision-based method,these decreases to 0.1227±0.325 meters and 2.2017±0.536 degrees, respectively. Thus, results indicate that the proposed methodimprove accuracy and efficiency of AR technologies.

Augmented Reality is a technology that can be used to visualize underground facilities. Although,processing in sensor-based methods is sufficiently fast and simple, they lack the precision required for this purpose. Despite the fact that noise elimination and sensor integration using Kalman filter improves accuracy to some degree, it still does not reach the required accuracy. The present article sought to improve the accuracy of augmented reality in underground infrastructureusing targets. Results indicated that the machine vision and vision-based methods improve the accuracy. In drillings, third dimension (accuracy of height measurements) is as crucial as other parameters, thusit is suggested that future researches consider this not as a descriptive parameter, but as a three dimensional parameter to reach 3dimensional visualization.

Since incorrect excavations have resulted in extensive and irreparable financial and physical losses, therefore different drillings require having accurate information about the status of the infrastructures. Ubiquitous Geospatial Information System (UBGIS) as a new generation of Geospatial Information System (GIS) can be a good solution to avoid such problems.  Augmented Reality (AR) is the next generation of real-world 3D visualization that can be used to visualize this infrastructure on smart phones. These days, due to recent advances in the mobile phone hardware industry, such as increased processing power and the availability of various sensors such as GPS, accelerometer, gyroscope, compass, camera and mobile phone display, AR is easily available to developers and users. The exterior orientation parameters of camera are required for AR and tracking. These parameters can be achieved by sensors embedded in smart phones. So overview of type, performance and accuracy of sensors is critical. This paper aims to examine the performance and accuracy of the types of sensors in the smartphone in order to visualize the underground infrastructure using augmented reality technology. For this purpose, two smartphones Samsung Galaxy S4 and iPhone4 have been reviewed. The Samsung S4 also has less radial distortion than the iPhone 4 (about half) and it is possible to correct it linearly. The best accuracy of GPS is about 10-20 m. The average of compass error is about 10 degrees in flat regions and about 30 degrees in regions surrounding with high buildings. Gyroscope values drift about 3-4 degrees per second and increase gradually. Also heading values in regions surrounding with high buildings have less accuracy. Generally choosing the best sensor and smartphone depend on usage. Also Samsung Galaxy S9 and iPhone 8 as the new generation of these two operating systems are compared.

Today, ubiquitous systems has been growing to become the new generation of Geospatial Information Systems (GIS). In Ubiquitous GIS, servicing capabilities to any user, at any time, in any location, using any device, and in any condition are provided. Advances in Information Technology (IT) industry have led to the advent of low-cost three dimensional data acquisition technologies (e.g. Microsoft Kinect). However, there are some deficiencies and limitations in utilizing these novel technologies in Ubiquitous GIS environments. Among them is the lack of information about the three dimensional topological relations between identified objects. Moreover, the current data models for the extraction of topological relations in ubiquitous environments are not sufficient. Therefore, the aim of this study is to extract the topological relations between objects sensed by Microsoft Kinect by designing a novel Ubiquitous GIS Data Model (UGDM). To obtain the extent of each object, the bounding box algorithm has been employed. The results of analyses on the acquired data from Kinect sensor show the success of the proposed data model in topological information .Ability to use spatial information at any time and places based on the use of IT and different infrastructures like sensor network (SN), Internet, and communication devices used in the system must be arranged by a ubiquitous data model to make different services at any time, any place, by any device and data for any user. Spatial relationships between objects, containing the fundamental information relating to the environment. Among these spatial relationships can be noted such as directional relationships, like: “left”,”right”,” above”, and “bottom” or distance based relations such as: “near” or “far”. The topology relationships used more in GIS desired location can be also noted such as: “inside”, “intersection”, “touch”, and “outside” and etc. Extraction of relationships between different features are done on the basis of data modeling techniques. Since the traditional data models don’t cover capabilities of the ubiquitous computing tools used in ubiquitous GIS to extract 3D relationships, we discuss the creation of facilities for the extraction of spatial relationships in ubiquitous GIS. A new generation of GIS is ubiquitous GIS, which in this generation service functionality to any user, at any given time and place. With the advancement of new technologies in capturing 3D point clouds of the environment, general changes are created in ubiquitous computing. The sensors used to obtain environmental information is provided in this generation are often inexpensive sensors. One of the important issues associated with this generation is the extraction of 3D topological relationships to interact with the environment better, which these sensors don’t have this ability to extract 3D topological relationships. To provide such a ubiquitous data model it is required to use software and hardware standards in order to exchange information in different parts of the ubiquitous model properly. The goal of this article is providing a ubiquitous data model based on the predefined standards to extract spatial relations. Spatial relations include directional, projection, distance and topological manners examined in this article. Because there is no unique method for extracting different spatial relations topological relations. Developing a spatial language as Ubi-OCL for defining different elements used in the data model provides an interoperable connection between the user and the data model. In this language the user can select the type of sensor and spatial relations to be extracted. For evaluating the presented data model, topological relations extracted will be presented in the article. As conclusion this article shows the different results with different sensors, the bounding box method is used for processing Kinect’s point cloud for extracting spatial relations according to provided data model.

Although close range photogrammetry has not been a common approach in the Civil Engineering field especially the displacement monitoring of large scale constructions, many project successfully used this approach and as a result this method have a high potential in this application. Nowadays, as a result of improving the computer vision and photogrammetry techniques implemented in many software and high resolution images captured by the off-the-shelf digital cameras, the use of progressive photogrammetric techniques instead of traditional displacement methods become more resendable. This paper aims to explain a novel method for monitoring of the large scale constructions based on visual inspection. This method can specify the displacement of the constructions based on photogrammetric and computer vision methods using drones. The evaluation of the proposed system is done on long wall in two epochs with a short time interval. Having known the zero displacement for this construction, the difference between the coordinates of some sample points obtained in first epoch and the second epoch, 1.89 mm, shows the accuracy of the proposed system in detecting displacement. Moreover, the accuracy of this photogrammetric method was investigated by developing a tool which manually provides a known displacement between two points in the second epoch. The displacements of these points were estimated using the proposed method and compared with the known displacement. The accuracy for this method, less than 2 mm, can confirm the capability of this method for such applications.

3D models of ancient sites are produced and utilized for different purposes such as research, restoration and renovation of valuable ancient objects, creation of virtual museums and documentation of ancient sites. Nowadays, Geomatics techniques, as the most efficient methods for geometrical measurements, analysis and interpretations concerning issues in cultural heritage, are applied to produce geometric and thematic information. Buildings are susceptible to change and damage through the passage of time due to natural agents and disasters such as rain, wind, earthquake, flood, or damages imposed by human beings. The characteristics of these changes in some buildings with ancient value bear special importance. The first step to create 3D models, provide the information about ancient monuments and record them with documents is having accurate maps of their present condition to be able to add other information like type of construction materials. Special techniques should be employed to provide maps with high accuracy, in addition to other characteristics such as spending the least expense and time for continuous map production. The process of changes are recognized by comparing maps from different time spans based on which due decisions can be made. To provide these maps many different techniques have been used since past such as traditional surveying (using the usual total stations), photogrammetry (especially close-range photogrammetry) and laser scanners. In comparison to other techniques, photogrammetry has unique characteristics in documentation of ancient sites. No need to contact with the feature, the possibility to obtain the information of texture and color and the compliance of these characteristics with the 3D output data, high flexibility of this method to access the desired accuracy in measurements and its potential of access to accuracy at micrometer level as well as capability of low expense observations and archiving images, are parameters that have given rise to the more usage of techniques of photogrammetry in the modelling of ancient sites. Yet, the usual techniques of photogrammetry sometimes have limitations, for example, in rare cases of inaccessible features. As a result, the requirement to obtain accurate information from features, especially in dangerous and remote areas, and also, the necessity to economize expense and time have led to the usage of UAV-based photogrammetry.
UAV-based photogrammetry is a combination of aerial photogrammetry and close-range photogrammetry in which there is a sensor that can be a metric or non-metric camera or any other data collection tool. The images are acquired from low height. Access to imaging stations with appropriate angle toward all parts of a feature and low height of flight, result images with high spatial resolution, which consequently, bring about more accurate and precise 3D information from earth. Different categorizations have been presented for UAVs based on different criteria and applications. To mention some of these criteria we can refer to the criterion of flexibility, fixed or rotating blades or wings in UAVs and their source of energy. Based on the categorizations of platforms regarding this research, which is ancient sites, it is obvious at first glance that UAVs with fixed wings, fixed or semi-flexible parachutes and wingless are practically of no use due to low flexibility in flying and imaging , and also limited space of flight. Therefore, low expense, high flexibility and appropriate time of flight have contributed to the suitability of quadrotors as the best option among all systems with rotating blades in this research.
Low expense for production, no need to airports and long runways and better maneuverability are some particular parameters and characteristics of the functionality of UAVs. There are factors that limit the function of UAVs, such as instability while flying due to light weight, limited source of supply, limitation to carry bigger and more accurate measuring tools and requiring longer time for imaging, processing and calculations. Fortunately, all these limitations can be modified to some extent by an appropriate network design. In spite of all aforementioned capabilities of UAV systems, no specific standards have been designed to utilize them. Therefore, it is obviously necessary to investigate the feasibility of the usage of these systems, and to design appropriate networks to locate them in proper points to obtain images for photogrammetry.
Thus, the need for high accuracy in UAV-based photogrammetry for documentation and restoration of ancient sites necessitates more concern for the network geometry to achieve the desired accuracy. This article presents appropriate method for optimum locations of UAV for imaging. The proposed method for the optimal locations of UAV is based on the ellipsoid fitted on object, principles and constraints of photogrammetry network design and finally by exploring hidden areas. The results from images taken from a cultural heritage site showed that the number of images was reduced almost 4 times by applying network design principles. Consequently, the speed of 3D modelling would be increased almost eight times by applying the proposed method.

Accurate positioning is an important problem in many fields especially Geographic Information System (GIS). With the advent of ubiquitous computing, a vast and massive change took place in various technologies, and a new generation of GIS, called ubiquitous GIS (UBGIS), was created. One of the most important aspects transformed by ubiquitous computing is in positioning process. ubiquitous GIS and its components provide an environment where position information can be accessed both inside and outside. Computer Vision and Vision based approaches could be a good and appropriate solution to improve positioning accuracy in a Ubiquitous GIS. Using simple and well-known objects such as targets is an appropriate method. Target recognition is important in determining the center and code of the target. In recent years, especial kind of targets called Coded Targets are considered in different fields of vision based approaches and the demand for a Coded Target guaranteeing automatic, error-free correspondence and accurate image point measurement, has been dramatically increased. Therefore, automatically detecting, matching, and determining the center coordinates of Coded Targets are critical issues. Due to various factors in the environment, automatic execution of this process is very difficult and complex or accuracy and speed are not suitable. This paper aims to propose a new method using feature based matching algorithms for automatically recognizing Coded Targets and identifying their centers with sub-pixel accuracy, which can be used to enhance positioning accuracy in ubiquitous GIS. To achieve this aim, feature based matching algorithms and combining local feature detectors and descriptors like SIFT, SURF, and AKAZE are used to find corresponding image points and automatically recognition of Coded Targets. Therefore, suitable matching algorithm is chosen by comparing different matching algorithms. Results show that the best matching algorithm for this usage is SIFT-SURF that means using SIFT descriptors and SURF detectors will lead to best matching results. Then K-means clustering method is applied to distinguish Coded Targets and extract code of target with respect to template targets that are stored in the database. The cluster with the largest number of corresponding points belongs to the template Coded Target. In second stage a bounding box around the matched Coded Target is considered by defining minimum and maximum coordinates of corresponding points around target, so that there is only one target in this boundary. Then the image is cut in this boundary to firstly increase the speed of the calculation, because the search area for an image gets smaller and secondly reduce the possibility of mistake, because the other features and targets in the cut image are almost eliminated. Then center coordinates of Coded Targets are computed by finding contours in this bounding box and fitting a Hough ellipse to central ellipse of target. Finally, the center of this fitted ellipse is computed as the center of Coded Target. The results of implementing the methods are compared with well-known photogrammetry software called Agisoft (modelling and accurate measuring based on basic photogrammetry and computer vision). Results demonstrate sub pixel accuracy {0.574, 0.496 pixel} in center determination in X and Y direction respectively and success possibility of 63% in code recognition.

Spatial relationships between objects, containing the fundamental information relating to the environment. Among these spatial relationships can be noted such as directional relationships, like: “left”,”right”,” above”, and “bottom” or distance based relations such as: “near” or “far”. The topology relationships used more in GIS desired location can be also noted such as: “inside”, “intersection”, “touch”, and “outside” and etc. Extraction of relationships between different features are done on the basis of data modeling techniques, since the traditional data models don’t cover capabilities of the ubiquitous computing tools used in ubiquitous GIS to extract 3D relationships, we discuss the creation of facilities for the extraction of spatial relationships in ubiquitous GIS. A new generation of GIS is 3D ubiquitous GIS, which in this generation service functionality to any user, at any given time and place. With the advancement of new technologies in capturing 3D point clouds of the environment, general changes are created in ubiquitous computing. The sensors used to obtain environmental information is provided in this generation are often inexpensive sensors. One of the important issues associated with this generation is the extraction of 3D topological relationships to interact with the environment better, which these sensors don’t have this ability to extract 3D topological relationships. The purpose of this paper is to extract topological relations by smart sensors used in ubiquitous computing. . The methodology used in this paper, is taking advantage of bounding box algorithm for mining the scope of any object in depth, length and the height. The results obtained from Kinect sensor shows the possibility of topological relationships extraction based on the smart sensors in ubiquitous GIS.

Urban infrastructure is one of the most important fields in different countries and the developing of a city largely depends on the proper infrastructure management. Therefore, a proper way of monitoring, controlling, planning and managing these infrastructures is essential. In this issue, finding the accurate positions of facilities such as pipes is a difficult problems due to the non-visibility of these infrastructures. Ubiquitous computing and new developments in geospatial information systems (GIS) can be a solution to such problems. This paper aims to describe the role of Ubiquitous GIS in underground infrastructure management and review modern GIS technologies and Ubiquitous computing in urban infrastructure. In this paper a variety of technologies in the field of urban infrastructure using ubiquitous computing is explained in three categories including infrastructures, sensors and services. Compared with the existing technologies in position estimation of underground utilities, it can be concluded that the exploiting of RFID technology is better than previous methods in this field. Also, this study reviles that in such applications and field inspection augmented reality methods are more suitable than Mobile GIS methods.