Impact of different trajectories on extrinsic self-calibration for vehicle-based mobile laser scanning systems

The trend toward further integration of automotive electronic control units functionality into domain control units as well as the rise of computing-intensive driver assistance systems has led to a demand for high-performance automotive computation platforms. These platforms have to fulfill stringent safety requirements. One promising approach is the use of performance computation units in combination with safety controllers in a single control unit. Such systems require adequate communication links between the computation units. While Ethernet is widely used, a high-speed serial link communication protocol supported by an Infineon AURIX safety controller appears to be a promising alternative. In this paper, a high-speed serial link IP core is presented, which enables this type of high-speed serial link communication interface for field-programmable gate array–based computing units. In our test setup, the IP core was implemented in a high-performance Xilinx Zynq UltraScale+, which communicated with an Infineon AURIX via high-speed serial link and Ethernet. The first bandwidth measurements demonstrated that high-speed serial link is an interesting candidate for inter-chip communication, resulting in bandwidths reaching up to 127 Mbit/s using stream transmissions

Automatic Real-Time Pose Estimation of Machinery from Images

The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than [Formula: see text] with the translation components and accuracies higher than [Formula: see text] with the rotation components. As a result, 3D point accuracies higher than [Formula: see text] were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors

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

Spotted fever group rickettsiae and Anaplasma phagocytophilum in Borrelia burgdorferi sensu lato seropositive individuals with or without Lyme disease: A retrospective analysis

BACKGROUND The Ixodes ricinus tick is the main vector of Borrelia burgdorferi and tick-borne encephalitis virus in Switzerland. Spotted fever group Rickettsiae (SFG) and Anaplasma phagocytophilum have been detected in Swiss ticks, however, information about the extent and clinical presentation of these infections in humans is scant. METHODS Indirect fluorescent antibody tests for SFG rickettsiae and Anaplasma phagocytophilum were performed on serum samples of 121 Borrelia burgdorferi seropositive patients with and without Lyme disease and 43 negative controls. RESULTS Out of 121 Borrelia burgdorferi seropositive individuals, 65 (53.7%) were seropositive for IgG and 15 (12.4%) for IgM antibodies to SFG rickettsiae. IgM antibodies were detected more frequently in early-than in late-stage of Lyme disease (12 out of 51 and 2 out of 49; respectively; p ​= ​0.0078). Significantly higher IgG antibody titers against SFG rickettsiae were found in patients with late-stage compared to patients with early-stage Lyme disease (mean titer 1:261 and 1:129, respectively; p ​= ​0.038). This difference was even more pronounced in patients with acrodermatitis chronica atrophicans compared to patients with early stage of Lyme disease (mean titer 1:337 and 1:129, respectively; p ​= ​0.009).In patients presenting with fatigue, headache and myalgia, the prevalence of IgG antibodies against SFG rickettsiae was significantly higher (7 out of 11; 63.6%) than in Borrelia burgdorferi seropositive individuals without clinical illness (1 out of 10; 10%; p ​= ​0.024). IgG antibodies to Anaplasma phagocytophilum were detected in 12 out of 121 individuals (9.9%), no IgM antibodies were found. CONCLUSION Infections with SFG rickettsiae and Anaplasma phagocytophilum are underdiagnosed and should be ruled out after a tick bite. Further studies are needed to elucidate the possible causative role of SFG rickettsiae for myalgia, headache and long-lasting fatigue after a tick bite and to determine the necessity for an antibiotic treatment

COMBINING HOLOLENS WITH INSTANT-NERFS: ADVANCED REAL-TIME 3D MOBILE MAPPING

This work represents a large step into modern ways of fast 3D reconstruction based on RGB camera images. Utilizing a Microsoft HoloLens 2 as a multisensor platform that includes an RGB camera and an inertial measurement unit for SLAM-based camera-pose determination, we train a Neural Radiance Field (NeRF) as a neural scene representation in real-time with the acquired data from the HoloLens. The HoloLens is connected via Wifi to a high-performance PC that is responsible for the training and 3D reconstruction. After the data stream ends, the training is stopped and the 3D reconstruction is initiated, which extracts a point cloud of the scene. With our specialized inference algorithm, five million scene points can be extracted within 1 second. In addition, the point cloud also includes radiometry per point. Our method of 3D reconstruction outperforms grid point sampling with NeRFs by multiple orders of magnitude and can be regarded as a complete real-time 3D reconstruction method in a mobile mapping setup

Semantic segmentation with small training datasets: A case study for corrosion detection on the surface of industrial objects

In this research, we investigate possibilities to train convolutional neural networks with a small dataset for semantic segmentation, while achieving the best possible model generalization. In particular, we want to segment corrosion on the surface of industrial objects. In order to achieve model generalization, we utilize a selection of established and advanced strategies, i.e. Self-Supervised-Learning. Besides radiometric- and geometric-based data augmentation, we focus on model complexity regarding encoder and decoder, as well as optimal pretraining. Finally, we evaluate the best performing model against a pixel-wise random forest classification. As a result, we achieve an f1-score of 0.79 for the best performing model regarding the segmentation of corrosion

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

Image-to-image translation for enhanced feature matching, image retrieval and visual localization

The performance of machine learning and deep learning algorithms for image analysis depends significantly on the quantity and quality of the training data. The generation of annotated training data is often costly, time-consuming and laborious. Data augmentation is a powerful option to overcome these drawbacks. Therefore, we augment training data by rendering images with arbitrary poses from 3D models to increase the quantity of training images. These training images usually show artifacts and are of limited use for advanced image analysis. Therefore, we propose to use image-to-image translation to transform images from a rendered domain to a captured domain. We show that translated images in the captured domain are of higher quality than the rendered images. Moreover, we demonstrate that image-to-image translation based on rendered 3D models enhances the performance of common computer vision tasks, namely feature matching, image retrieval and visual localization. The experimental results clearly show the enhancement on translated images over rendered images for all investigated tasks. In addition to this, we present the advantages utilizing translated images over exclusively captured images for visual localization