703 Koch’s triangle voltage mapping for cryoablation of slow pathway in children: preliminary data of a novel high density technique

Aims Different authors have described three-dimensional (3D) voltage mapping of the Koch’s triangle (KT) in order to find low-voltage bridges (LVBs) as targets for a successful transcatheter ablation (TCA) of the slow pathway (SP) in children. Recently, the advisor high density (HD) Grid™ mapping catheter was introduced as new multipolar catheter for HD mapping. The aim of the study was to describe our preliminary experience with the use of HD Grid™ catheter in LVB and electrophysiologically guided cryoablation of SP in children. Methods and results Twenty-one children (mean age 13 ± 3 years) with atrioventricular nodal re-entrant tachycardia (AVNRT) underwent cryoablation of SP guided by voltage HD mapping of the KT using HD Grid™ catheter. In order to better highlight the differences with conventional mapping, point collection was performed in each patient with this new multipolar catheter and with a quadripolar catheter. The conventional mapping collected 871 ± 262 points and used 211 ± 80 points in 887 ± 275 s, whereas HD mapping collected 7468 ± 2947 points, using 604 ± 165 points in 513 ± 181 s (P < 0.001). Moreover, the LVB area mapped with HD Grid™ was about one-half smaller and clearly delineated. Cryoablation acute success rate was 100%. Overall median fluoroscopy exposure was 0.08 (0.01–5.42) µGy/m2, with a median fluoroscopy time of 0.1 (0.0–0.6) min. During the follow-up (4.8 ± 3.7 months), there were no recurrences. No complications occurred. Conclusions Our preliminary experience shows that HD mapping is faster and offers higher spatial resolution and definition. Procedural time can be reduced maintaining the TCA safe, with reduced fluoroscopy use, and successful.

POINT CLOUD REGISTRATION AND ACCURACY FOR 3D MODELLING - A REVIEW

Geoinformation is a surveying and mapping field where topography and details on the ground are spatially mapped. The point cloud is one of the data types that is used for measurement and visualisation of Earth features mapping. Point cloud could come from a different source such as terrestrial laser scanned or photogrammetry. The concepts of terrestrial laser scanning and photogrammetry surveying are elaborated in this paper. This paper also presents the method used for point cloud registration; Iterative Closest Point (ICP) and Feature Extraction and Matching (FEM) and the accuracy of laser scanned, and photogrammetric point cloud based on the previous experiments. Experimental analysis conducted in the previous study shows an impressive result on laser scanned point cloud with very mínimum errors compared to photogrammetric point cloud.

Deep Localization of Static Scans in Mobile Mapping Point Clouds

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

Mapping for Indoor Walking Path Using Mobile Laser Scanning

Walkability is one of the keys in developing a sustainable city. These days, many cities have considered enhancing walkability for pedestrian paths to ensure the seamless walking experience for people to reach their destination. Therefore, it is very important to have a good walking environment so people will find walking pleasant. However, there was a lack of studies attempting to include indoor walking environments in their walkability analysis. Most of them only consider outdoor walking paths. This might be due to the difficulties in modelling the indoor walking environment. With the advance technology of laser scanning, it might be possible to develop an indoor walking path by using point clouds collected for a building. The usage of point clouds could make it easier to segment the building elements and obstacles in an indoor environment. In order to produce an indoor map, it is important to reconstruct the building elements such as wall, ceiling, window and door. Therefore, this paper aims to generate the indoor walking path using laser scanning point clouds showing all the options to the pedestrians.Keywords: Walkability, indoor mapping, point cloud, laser scanning, mobile laser scanning

Manhole Cover Detection on Rasterized Mobile Mapping Point Cloud Data Using Transfer Learned Fully Convolutional Neural Networks

Large-scale spatial databases contain information of different objects in the public domain and are of great importance for many stakeholders. These data are not only used to inventory the different assets of the public domain but also for project planning, construction design, and to create prediction models for disaster management or transportation. The use of mobile mapping systems instead of traditional surveying techniques for the data acquisition of these datasets is growing. However, while some objects can be (semi)automatically extracted, the mapping of manhole covers is still primarily done manually. In this work, we present a fully automatic manhole cover detection method to extract and accurately determine the position of manhole covers from mobile mapping point cloud data. Our method rasterizes the point cloud data into ground images with three channels: intensity value, minimum height and height variance. These images are processed by a transfer learned fully convolutional neural network to generate the spatial classification map. This map is then fed to a simplified class activation mapping (CAM) location algorithm to predict the center position of each manhole cover. The work assesses the influence of different backbone architectures (AlexNet, VGG-16, Inception-v3 and ResNet-101) and that of the geometric information channels in the ground image when commonly only the intensity channel is used. Our experiments show that the most consistent architecture is VGG-16, achieving a recall, precision and F2-score of 0.973, 0.973 and 0.973, respectively, in terms of detection performance. In terms of location performance, our approach achieves a horizontal 95% confidence interval of 16.5 cm using the VGG-16 architecture.

Study on Clearance-Rubbing Dynamic Behavior of 2-DOF Supporting System of Magnetic-Liquid Double Suspension Bearing

As a new type of suspension bearing, Magnetic-Liquid Double Suspension Bearing (MLDSB) is mainly supported by electromagnetic suspension and supplemented by hydrostatic supporting. Its bearing capacity and stiffness can be greatly improved. Because of the small liquid film thickness (it is smaller 10 times than air gap), the eccentricity, crack, bending of the rotor, and the assembly error, it is easy to cause a clearance-rubbing fault between the rotor and stator. The coating can be worn and peeled, the operating stability can be reduced, and then it is one of the key problems of restricting the development and application of MLDSB. Therefore, the clearance-rubbing dynamic equation of 2-DOF system of MLDSB is established and converted into Taylor Series form and the nonlinear components are retained. Dimensionless treatment is carried out by dimensional normalization method. Finally, the rotor displacement response under different rotor eccentricity ratio and rotating speeds is numerically simulated. The studies show that the trajectory of the rotor is periodic elliptic without clearance-rubbing phenomenon when the eccentricity ratio is less than 0.2, while the rotor is greatly affected by the rotation speed and a variety of motions, such as single-period, quasi-period, double-period and chaos, are presented when greater than 0.3. Within the largest range of rotating speed and eccentricity ratio, the rotor presents the single-period trajectory, and then the number of Poincare mapping point is 1, without a clearance-rubbing fault. When the rotational speed is in the scope of (9, 13) krpm and the eccentricity ratio is in the scope of (0.27, 0.4), the number of Poincare mapping point is more than one, the maximum dimensionless rubbing force is −5.7, and then clearance-rubbing fault occurs. The research can provide a theoretical basis for the safe and stable operation of MLDSB.