Review of Mapping with Altitude: Designing 3D Maps

Permanent His bundle pacing is feasible and effective in patients with atrioventricular block or left bundle branch block. However, pacing thresholds to capture the distal His bundle is often higher. Recently left bundle branch area pacing (LBBP) has been shown to be feasible by advancing the lead transvenously, deep into the interventricular septum to reach the left ventricular endocardial surface. In this article we describe the utility of three dimensional (3D) mapping to achieve LBBP. Ensite Precision (Abbott) mapping system was used to perform LBBP. A decapolar catheter was used to create 3D map of right atrium and right ventricle (RV). Regions of interest (His bundle, potential LBBP sites of interest in RV) were tagged in the 3D map. The LBBP lead was implanted utilizing the 3D map. The lead depth in the septum was assessed in the 3D map. LBBP was performed in three patients: chronic LBBB and intermittent 2:1 atrioventricular block; atrioventricular (AV) node ablation and conduction system pacing; and bifascicular block and intermittent AV block in a patient with severe left ventricular hypertrophy. LBBP was successful in all three patients. The lead depth in the interventricular septum was 12, 11, and 21 mm, respectively as assessed by 3D mapping. Three-dimensional mapping was helpful in achieving LBBP in patients with LBBB, severe left ventricular hypertrophy or during AV node ablation. 3D mapping also facilitated easy assessment of lead depth during and after lead fixation. 3D mapping techniques may be a valuable tool to reduce the learning curve of implanters with minimal experience in LBBP.

2019 APHRS expert consensus statement on three-dimensional mapping systems for tachycardia developed in collaboration with HRS, EHRA, and LAHRS.

3D technology is used in GIS maps to create descriptive graphics that accurately depict the size of real-world items. In a wide variety of various areas, 3D models aid in appearance and survey. For example, 3D maps may depict the height of a building or a mountain in addition to its location. 3D tools must be utilized in conjunction with 2D GIS and then visualized in a 3D environment. The current UPNM campus map accessible to students and staff is not sufficiently helpful and hardly identified its whereabouts. This project was undertaken to develop a web-based 3D campus map and analyze specifically the campus’s area zoning. This study uses a personal computer with Esri CityEngine software to develop the 3D campus map and ArcGIS Online to publish the web-based 3D campus map. The data was acquired by downloading the base map of UPNM from OpenStreetMap (OSM). Completion of data processing results in a web-based 3D UPNM campus map with area zoning. The finding shows that the 3D map, using the Geographical Information System (GIS) platform, is more informative and attractive than the 2D map due to the attribute and spatial data. In addition, the capabilities of the 3D map to visualize and show the campus area and area zoning in the map can be managed according to the structural information. All in all, it can be concluded that this web-based 3D map contributes towards the sustainability of the UPNM infrastructure and campus while facilitating campus maintenance and rehabilitation.

Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment

Two-dimensional (2D) mapping using different chromatographic separations coupled with mass spectrometry is a rapid and simple method for the analysis of a mixture using conventional liquid chromatography mass spectrometry. The 2D map could be created from two different chromatograms obtained with the same detector and different columns or separation methods. In this study, 2D mapping was applied to the analysis of components contained in Panax ginseng, and was evaluated in terms of its effectiveness in the separation of these components. The several glycosides included in Panax ginseng could not be sufficiently separated by one-dimensional chromatography with a reverse phase or a hydrophilic interaction chromatography (HILIC) column, but the components of Panax ginseng could be separated and visualized as a component pattern by 2D mapping. We showed that the components contained in the calli and their quantities were altered by the culture conditions in which the calli were grown by 2D mapping. 2D mapping is expected to be a useful method for visualizing complex component patterns found in glycosides and unknown compounds in foods.

BackgroundThis study aimed to define the distribution and frequency of fracture lines and bone defects in displaced femoral neck fractures (DFNFs) using a three-dimensional (3D) mapping technique, and to investigate the factors associated with the area of bone defects in patients with DFNFs.MethodsThe data of 256 adult patients with DFNFs were retrospectively reviewed. Multiplanar reconstructions of the DFNFs were made using computed tomography (CT) images, and the DFNF fragments were virtually reduced to match a 3D model of the femoral neck. Subsequently, 3D mapping was performed by graphically superimposing all of the fracture lines and bone defects onto a femoral neck template. The 3D mappings were independently examined by two orthopedic surgeons, and the interobserver agreement was analyzed. For intraobserver analysis, one of the surgeons measured the mappings twice more, and the intraclass correlation coefficients (ICCs) were calculated. A linear regression analysis was conducted to explore bone defect area-related factors.ResultsThe cohort comprised 141 (55%) patients with left hip injuries and 115 (45%) patients with right hip injuries. On the 3D maps, the dense zones of the fracture lines were largely observed from the superior to the posterior part of the femoral neck, while the dense zone of the bone defect was primarily concentrated in the posterior part of the femoral neck. Only a few dense zones were located in the anterior and inferior parts of the femoral neck. An overlapping region between the fracture line and the bone defect was located in the 2.5th to 4.5th (5th) part of the 1/10 of the superior (posterior) femoral neck length. Both the fracture line and bone defect mapping techniques had good intra- and inter-observer reliability, with ICCs of 0.879 (0.977) and 0.780 (0.974), respectively. Garden type and age were positively correlated with bone defects, while simplified AO Foundation/Orthopaedic Trauma Association (AO/OTA) classification was negatively associated with bone defects.ConclusionsThe fracture lines and bone defects of the DFNFs were mainly located in the superior and posterior parts of the femoral neck, while an overlapping region was observed in the subcapital area of the femoral neck. 3D mapping is a reliable method for searching for DFNF features, and separately studying fracture lines and bone defects can further elucidate the morphology of these fractures. Bone defects in patients with DFNFs were associated with Garden type, simplified AO/OTA classification, and age.

Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution.

This study aimed to explore how a three-dimensional (3D) map affects the acquisition of survey knowledge in complex virtual environments (VEs). Subject navigated in virtual and real subway stations, where the virtual station was augmented with either a 3D floor map, a 3D building map, or neither. After subject's navigation in the station, they made the judgment of spatial relative directions between objects. The analyses of response time showed that participants responded faster when they were assisted by the 3D floor or building map than without a map aid in virtual stations. There was no significant difference between survey knowledge acquired in the virtual station augmented with the 3D floor or building map and in the real station. Participants with the 3D floor map performed best in spatial judgments. Implications of these findings to a 3D map design were discussed.

In order to move around automatically, mobile robots usually need to recognize their working environment first. Simultaneous localization and mapping (SLAM) has become an important research field recently, by which the robot can generate a map while moving around. Both two-dimensional (2D) mapping and three-dimensional (3D) mapping methods have been developed greatly with high accuracy. However, 2D maps cannot reflect the space information of the environment and 3D mapping needs long processing time. Moreover, conventional SLAM methods based on grid maps take a long time to delete the moving objects from the map and are hard to delete the potential moving objects. In this paper, a 2D mapping method integrating with 3D information based on immobile area occupied grid maps is proposed. Objects in 3D space are recognized and their space information (e.g., shapes) and properties (moving objects or potential moving objects like people standing still) are projected to the 2D plane for updating the 2D map. By using the immobile area occupied grid map method, recognized still objects are reflected to the map quickly by updating the immobile area occupancy probability with a high coefficient. Meanwhile, recognized moving objects and potential moving objects are not used for updating the map. The unknown objects are reflected to the 2D map with a lower immobile area occupancy probability so that they can be deleted quickly once they are recognized as moving objects or start to move. The effectiveness of our method is proven by experiments of mapping under dynamic indoor environment using a mobile robot.

This paper proposes a novel positioning technique that can be used for vehicles and pedestrians in urban environments in which there are large global navigation satellite system (GNSS) positioning errors because of multipath signals. In GNSS positioning, invisible satellites that are obstructed by buildings emit reflection and diffraction signals, which are called non-line-of-sight (NLOS) multipath signals. These cause major positioning errors in GNSS positioning. Thus, to mitigate the NLOS multipath error, NLOS signals must be identified from all received GNSS signals. 3D environmental maps can be used to identify NLOS signals. In some studies, 3D maps are used to improve GNSS positioning accuracy. However, the previously proposed techniques need a long convergence time to estimate the user position. In this study, we propose a positioning technique that can directly combine GNSS pseudorange-based positioning and 3D environmental maps to improve the positioning accuracy in urban environments where NLOS causes major positioning errors. The key idea behind this paper is to estimate user position using likelihood of position hypotheses computed from GNSS pseudo-ranges which consists of only LOS signals. To determine the NLOS GNSS signals at the user position, it is in turn necessary to accurately predetermine the position before simulation; we solve this problem by using a particle filter. We combine two likelihood estimation methods to determine the likelihood of each particle. The first method uses the Mahalanobis distance and the second method uses the degree of matching of availability of LOS signals. Differing hypotheses of user position are represented by these particles, and for each of these, the likelihood can be evaluated using only the LOS pseudoranges determined from the 3D map. To confirm the effectiveness of the proposed technique, a positioning test was performed in a real-world urban-canyon environment. Using the proposed method, the distribution of the particles converged to within 1 m after 6 s and iterated resampling. In conclusion, our proposed method is suitable and effective for estimating accurate user positioning in urban canyons where conventional GNSS positioning can cause large positioning errors.

Metric scale obstacle detection, which detects obstacles and measures the distances to them with a metric scale, is a key function in autonomous driving. A monocular camera is inexpensive and effective for detecting objects in images. However, it cannot measure the distances to objects with a metric scale because it can estimate only relative distance. 3D point cloud maps can determine the distances of fixed objects in the 3D map; however, they cannot detect non-fixed obstacles that are not in the 3D map. Therefore, we developed a new method for detecting non-fixed obstacles using a monocular camera and 3D point cloud maps. We used a semantic segmentation neural network (NN) for detecting obstacles and an image-guided depth completion NN for densifying a sparse depth map with a metric scale. We proposed a multitask NN that three-dimensionally reconstructed non-fixed obstacles using the shape information obtained by the semantic segmentation NN. The detection accuracy of the proposed multitask NN was 1.3 times higher than that of a single-task method. Moreover, our robot avoided obstacles using the proposed NN.

The potential role of Autotaxin (ATX) in physiological and pathological processes turned it in an attractive drug target for pharmacological therapeutic development. However, potent and selective non-lipid as well lipid inhibitors of ATX are currently not available as drugs. In this paper we tried to review all the known progress on ATX inhibition using two dimensional (2D)-Quantitative Structure Activity Relationship (QSAR) and three dimensional (3D) mapping techniques. Furthermore, we tried to compare and extract conclusions analyzing with 3D mapping techniques vastly diverse structures of non-lipid ATX inhibitors which have been reported in patents. McGowan's Volume (MgVol) molar volume and Molar Refractivity (MR) of substituents seems to govern the ATX inhibition. 3D-mapping results point to the role of steric properties (Volume and Polar Surface Area-PSA). Steric factors are obviously important. The role of hydrophilicity was also highlighted. Electronic parameters are not found to be present.

BackgroundT2 mapping is a magnetic resonance imaging technique measuring T2 relaxation time, which increases with the myocardial tissue water content. Myocardial edema is a component of acute cellular rejection (ACR) after heart transplantation. This pilot study compares in heart transplantation recipients a novel high resolution 3-dimensional (3D) T2-mapping technique with standard 2-dimensional (2D) T2-mapping for ACR detection.MethodsConsecutive asymptomatic patients (n = 26) underwent both 3D T2 mapping and reference 2D T2 mapping magnetic resonance imaging on the day of endomyocardial biopsy (EMB). 3D T2 maps were obtained at an isotropic spatial resolution of 1.72 mm (voxel volume 5.1 mm3). 2D and 3D maps were matched anatomically, and maximum segmental T2 values were compared blinded to EMB results. In addition, all 3D T2 maps were rendered as 3D images and inspected for foci of T2 elevation.ResultsT2 values of segments from 2D and reformatted 3D T2 maps agreed (p > 0.5). The highest 2D segmental T2 values were 49.9 ± 4.0 ms (no ACR = 0R, n = 18), 48.9 ± 0.8 ms (mild ACR = 1R, n = 3), and 65.0 ms (moderate ACR = 2R). Rendered 3D T2 maps of cases with 1R showed foci with significantly elevated T2 signal (T2 = 58.2 ± 3.6 ms); 5 cases (28%) in the 0R group showed foci with increased T2 values (>2 SD above adjacent tissue) that were not visible on the 2D T2 maps.ConclusionsThis pilot study in a small cohort suggests equivalency of standard segmental analysis between 3D and 2D T2-mapping. 3D T2 mapping provides a spatial resolution that permits detection of foci with elevated T2 in patients with mild ACR.

Small-sized unmanned aerial vehicles (UAVs) have been widely investigated for use in a variety of applications such as remote sensing and aerial surveying. Direct three-dimensional (3D) mapping using a small-sized UAV equipped with a laser scanner is required for numerous remote sensing applications. In direct 3D mapping, the precise information about the position and attitude of the UAV is necessary for constructing 3D maps. In this study, we propose a novel and robust technique for estimating the position and attitude of small-sized UAVs by employing multiple low-cost and light-weight global navigation satellite system (GNSS) antennas/receivers. Using the "redundancy" of multiple GNSS receivers, we enhance the performance of real-time kinematic (RTK)-GNSS by employing single-frequency GNSS receivers. This method consists of two approaches: hybrid GNSS fix solutions and consistency examination of the GNSS signal strength. The fix rate of RTK-GNSS using single-frequency GNSS receivers can be highly enhanced to combine multiple RTK-GNSS to fix solutions in the multiple antennas. In addition, positioning accuracy and fix rate can be further enhanced to detect multipath signals by using multiple GNSS antennas. In this study, we developed a prototype UAV that is equipped with six GNSS antennas/receivers. From the static test results, we conclude that the proposed technique can enhance the accuracy of the position and attitude estimation in multipath environments. From the flight test, the proposed system could generate a 3D map with an accuracy of 5 cm.

Three-dimension (3D) maps visualization concept for mobile devices which can be used for navigation aid comes in to being as a result of the weakness perceived with the two dimension (2D) map. The representation of scene in 2D map is generally limited and lack interaction with the users. The interaction of 2D map is limited to the interpretation of symbols explained in the legend which not every user knows how to use efficiently. 3D map, on the other hand, is able to produce a realistic scene. 3D map delivered to mobile device through remote rendering will serves as a navigation aid. Unfortunately, visualization of 3D map in mobile devices is faced with a huge drawbacks when considering the small screen size mobile device and with low computing resources. This paper survey the optimum remote rendered techniques used for 3D visualization on mobile devices and provide a prototype of 3D mobile map that will be suitable as navigation aid. The 3D map that this paper is considering is different from the manipulated static aerial photo-realistic images that gives the illusion of 3D presentation.