Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensity-based approaches.Comment: 8 pages, 4 figures, 4 tables, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

Endothelin 1 levels in relation to clinical presentation and outcome of Henoch Schonlein purpura

<p>Abstract</p> <p>Background</p> <p>Henoch Schonlein purpura (HSP) is a common vasculitis of small vessels whereas endothelin-1 (ET-1) is usually reported elevated in vasculities and systematic inflammation. The aim of the present study was to investigate whether ET-1 levels are correlated with the clinical presentation and the outcome of HSP.</p> <p>Methods</p> <p>The study sample consisted of thirty consecutive patients with HSP. An equal number of healthy patients of similar age and the same gender were served as controls. The patients' age range was 2–12.6 years with a mean ± SD = 6.3 ± 3 years. All patients had a physical examination with a renal, and an overall clinical score. Blood and urinary biochemistry, immunology investigation, a skin biopsy and ET-1 measurements in blood and urine samples were made at presentation, 1 month later and 1 year after the appearance of HSP. The controls underwent the same investigation with the exception of skin biopsy.</p> <p>Results</p> <p>ET-1 levels in plasma and urine did not differ between patients and controls at three distinct time points. Furthermore the ET-1 were not correlated with the clinical score and renal involvement was independent from the ET-1 measurements. However, the urinary ET-1 levels were a significant predictor of the duration of the acute phase of HSP (HR = 0.98, p = 0.032, CI0.96–0.99). The ET-1 levels did not correlate with the duration of renal involvement.</p> <p>Conclusion</p> <p>Urinary ET-1 levels are a useful marker for the duration of the acute phase of HSP but not for the length of renal involvement.</p

Diagnostic yield of next-generation sequencing in very early-onset inflammatory bowel diseases: a multicenter study (vol 12, pg 1104, 2021)

Transplantation and immunomodulatio

Aerial image sequence geolocalization with road traffic as invariant feature

The geolocalization of aerial images is important for extracting geospatial information (e.g. the position of buildings, streets, cars, etc.) and for creating maps. The standard is to use an expensive aerial imaging system equipped with an accurate GPS and IMU and/or do laborious Ground Control Point measurements. In this paper we present a novel method to recognize the geolocation of aerial images automatically without any GPS or (Inertial Measurement Unit) IMU. We extract road segments in the image sequence by detecting and tracking cars. We search in a database created from a road network map for the best matches between the road database and the extracted road segments. Geometric hashing is used to retrieve a shortlist of matches. The matches in the shortlist are ranked by a verification process. The highest scoring match gives the location and orientation of the images. We show in the experiments that our method can correctly geolocalize the aerial images in various scenes: e.g. urban, suburban, rural with motorway. Beside the current images only the road map is needed over the search area. We can search an area of 22500 km2 containing 32000 km of streets within minutes on a single cpu

Aerial image mosaicking with online calibration - A feasibility study

Compiling individual aerial images to a larger mosaic image is important for many remote sensing tasks, e.g. mapping. The standard way to address this problem is to orthorectify the image and later compile it together. For the orthorectification the orientation and the location of the camera has to be measured accurately and a Digital Elevation Model (DEM) is needed or ground control points have to be set manually. In this paper we present a feasibility study on an approach which works purely on original camera images without any GPS and/or IMU. The intern and extern camera parameters and the 3D feature points are calculated based on Structure from Motion (SfM). The ground surface is assumed to be flat and a plane is fitted on the 3D feature points. A virtual camera is calculated then which is perpendicular to the plane and the images are transformed into each other by a homography. We test this approach on image sequences captured by a standard DSLR camera

Fast Multiclass Vehicle Detection on Aerial Images

Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV)

Real-time mapping from a helicopter with a new optical sensor system

Ein neues optisches echtzeitfähiges real-time Sensorsystem auf einem Hubschrauber (4k System) für Einsätze bei Katastrophen, Großereignissen und anderen Überwachungsaufgaben ist nun als Prototyp einsatzfähig. Der Sensor wurde gewichtsoptimiert, klein und mit preisgünstigen Bauteilen in einem Pylon seitlich am Hubschrauber konzipiert. Es mussten jedoch aufgrund der geforderten Funktionalität und der Echtzeitfähigkeit real-time Fähigkeit hier Kompromisse eingegangen werden. Integriert sind neben der automatischen Orthophotoerstellung verschiedene thematische Prozessoren wie z.B. die automatische Verkehrsdatenextraktion. Der Sensor ist mit der neuesten Generation handelsüblicher High-End Kleinbildkameras bestückt, die je nach Anwendung konfiguriert werden können. Dabei decken die Kameras bestückt mit 50mm Objektiven ein Blickfeld von bis zu 104° ab, beim Einsatz von 100mm Objektiven werden Auflösungen bis zu 3.5 cm bei einer Flughöhe von 500m ü.G. erreicht. Zusätzlich kann mit einer Kamera der nahe Infrarotbereich erfasst werden oder ein hochaufgelöstes Video (4k Video) aufgenommen werden. In diesem Paper werden die Systemkomponenten, die technischen Eigenschaften des Sensors beschrieben sowie die verschiedenen Einsatzmöglichkeiten des Systems skizziert

Enhancing Road Maps by Parsing Aerial Images Around the World

In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks. In this paper we propose to exploit aerial images in order to enhance freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field parameterized in terms of the location of the road-segment centerlines as well as their width. This parameterization enables very efficient inference and returns only topologically correct roads. In particular, we can segment all OSM roads in the whole world in a single day using a small cluster of 10 computers. Importantly, our approach generalizes very well; it can be trained using only 1.5 km2 aerial imagery and produce very accurate results in any location across the globe. We demonstrate the effectiveness of our approach outperforming the state-of-the-art in two new benchmarks that we collect. We then show how our enhanced maps are beneficial for semantic segmentation of ground images

Performance of a real-time sensor and processing system on a helicopter

A new optical real-time sensor system (4k system) on a helicopter is now ready to use for applications during disasters, mass events and traffic monitoring scenarios. The sensor was developed light-weighted, small with relatively cheap components in a pylon mounted sideward on a helicopter. The sensor architecture is finally a compromise between the required functionality, the development costs, the weight and the sensor size. Aboard processors are integrated in the 4k sensor system for orthophoto generation, for automatic traffic parameter extraction and for data downlinks. It is planned to add real-time processors for person detection and tracking, for DSM generation and for water detection. Equipped with the newest and most powerful off-the-shelf cameras available, a wide variety of viewing configurations with a frame rate of up to 12Hz for the different applications is possible. Based on three cameras with 50mm lenses which are looking in different directions, a maximal FOV of 104° is reachable; with 100mm lenses a ground sampling distance of 3.5cm is possible at a flight height of 500m above ground. In this paper, we present the first data sets and describe the technical components of the sensor. The effect of vibrations of the helicopter on the GNSS/IMU accuracy and on the 4k video quality is analysed. It can be shown, that if the helicopter hoovers the rolling shutter effect affects the 4k video quality drastically. The GNSS/IMU error is higher than the specified limit, which is mainly caused by the vibrations on the helicopter and the insufficient vibrational absorbers on the sensor board