Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated

Pilot Study of a Radiation Oncology Telemedicine Platform

Purpose: A pilot study was undertaken to develop an integrated telemedicine platform for radiation oncology at Memorial Sloan-Kettering Cancer Center (MSKCC) and its regional sites. The platform consisted of a computer system with simultaneous display of multiple live data portals including 1) video-conferencing between physicians, 2) radiology, and 3) radiation treatment-planning system (RTPS). Methods and Materials: Two MSKCC regional centers were set up with a widescreen monitor, a dedicated computer, and a web camera with microphone. Each computer ran a Microsoft operating system, utilized video-conferencing software, and connected to the MSKCC Ethernet. This allowed for access to the health information system, radiology (web-based picture archiving and communication systems), RTPS, shared network drives and the internet. Results: After 3 months, physicians at two MSKCC sites were successfully able to implement the proposed telemedicine platform. A small sample of cases (prostate, breast, head and neck, and anal cases) were tested. Radiology images, radiation treatment volumes and plans, and portal images were reviewed. Side-by-side comparison of contouring techniques was performed. The platform allowed physicians to remotely review details of cases efficiently. The interactions of the telemedicine platform improved clinical understanding of each case and often resulted in contouring changes. Conclusion: From this experience, we feel that telemedicine could have a significant clinical impact on patient care, especially at centers with satellite clinics. The future goal of the system will be the development of a virtual tumor board for radiation oncologists. We envision the simultaneous display of multiple clinical components, including face photo, pathology, tumor images/videos of procedures, radiology, RTPS, and anatomy/contouring databases, on one screen surface

UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation

The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set

CNN-Based Initial Localization Improved by Data Augmentation

Image-based localization or camera re-localization is a fundamental task in computer vision and mandatory in the fields of navigation for robotics and autonomous driving or for virtual and augmented reality. Such image pose regression in 6 Degrees of Freedom (DoF) is recently solved by Convolutional Neural Networks (CNNs). However, already well-established methods based on feature matching still score higher accuracies so far. Therefore, we want to investigate how data augmentation could further improve CNN-based pose regression. Data augmentation is a valuable technique to boost performance on training based methods and wide spread in the computer vision community. Our aim in this paper is to show the benefit of data augmentation for pose regression by CNNs. For this purpose images are rendered from a 3D model of the actual test environment. This model again is generated by the original training data set, whereas no additional information nor data is required. Furthermore we introduce different training sets composed of rendered and real images. It is shown that the enhanced training of CNNs by utilizing 3D models of the environment improves the image localization accuracy. The accuracy of pose regression could be improved up to 69.37% for the position component and 61.61% for the rotation component on our investigated data set

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the ''simplest'' network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19 figures, 1 table; online supplement: 11 pages, 18 figures, 3 table

ReaxFF Reactive Force-Field Modeling of the Triple-Phase Boundary in a Solid Oxide Fuel Cell

In our study, the Ni/YSZ ReaxFF reactive force field was developed by combining the YSZ and Ni/C/H descriptions. ReaxFF reactive molecular dynamics (RMD) were applied to model chemical reactions, diffusion, and other physicochemical processes at the fuel/Ni/YSZ interface. The ReaxFF RMD simulations were performed on the H_2/Ni/YSZ and C_4H_(10)/Ni/YSZ triple-phase boundary (TPB) systems at 1250 and 2000 K, respectively. The simulations indicate amorphization of the Ni surface, partial decohesion (delamination) at the interface, and coking, which have indeed all been observed experimentally. They also allowed us to derive the mechanism of the butane conversion at the Ni/YSZ interface. Many steps of this mechanism are similar to the pyrolysis of butane. The products obtained in our simulations are the same as those in experiment, which indicates that the developed ReaxFF potential properly describes complex physicochemical processes, such as the oxide-ion diffusion, fuel conversion, water formation reaction, coking, and delamination, occurring at the TPB and can be recommended for further computational studies of the fuel/electrode/electrolyte interfaces in a SOFC

Coherence Phenomena in Charmonium Production off Nuclei at the Energies of RHIC and LHC

In the energy range of RHIC and LHC the mechanisms of nuclear suppression of charmonia are expected to be strikingly different from what is known for the energy of the SPS. One cannot think any more of charmonium produced on a bound nucleon which then attenuates as it passes through the rest of the nucleus. The coherence length of charmonium production substantially exceeds the nuclear radius in the new energy range. Therefore the production amplitudes on different nucleons, rather than the cross sections, add up and interfere, i.e. shadowing is at work. So far no theoretical tool has been available to calculate nuclear effects for charmonium production in this energy regime. We develop a light-cone Green function formalism which incorporates the effects of the coherence of the production amplitudes and of charmonium wave function formation, and is the central result of this paper. We found a substantial deviation from QCD factorization, namely, gluon shadowing is much stronger for charmonium production than it is in DIS. We predict for nuclear effects $x_2$ scaling which is violated at lower energies by initial state energy loss which must be also included in order to compare with available data. In this paper only the indirect J/Psi originating from decay of P-wave charmonia are considered. The calculated x_F-dependence of J/Psi nuclear suppression is in a good accord with data. We predict a dramatic variation of nuclear suppression with x_F in pA and a peculiar peak at x_F=0 in AA collisions at RHIC.Comment: 51 pages including 12 figures. Two references and comments are added at the en