scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals Assessment of Outdoor Thermal Comfort in Serbia’s Urban Environments during Different Seasons

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1084
Author(s):  
Milica Lukić ◽  
Dejan Filipović ◽  
Milica Pecelj ◽  
Ljiljana Crnogorac ◽  
Bogdan Lukić ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Urban Areas ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Urban Environments ◽  
Outdoor Thermal Comfort ◽  
Strong Impact ◽  
Data Set ◽  
Different Seasons ◽  
Novi Sad

The urban microclimate is gradually changing due to climate change, extreme weather conditions, urbanization, and the heat island effect. In such an altered environment, outdoor thermal comfort can have a strong impact on public health and quality of life in urban areas. In this study, three main urban areas in Serbia were selected: Belgrade (Central Serbia), Novi Sad (Northern Serbia), and Niš (Southern Serbia). The focus was on the temporal assessment of OTC, using the UTCI over a period of 20 years (1999–2018) during different seasons. The main aim is the general estimation of the OTC of Belgrade, Novi Sad, and Niš, in order to gain better insight into the bioclimatic condition, current trends and anomalies that have occurred. The analysis was conducted based on an hourly (7 h, 14 h, and 21 h CET) and “day by day” meteorological data set. Findings show the presence of a growing trend in seasonal UTCI anomalies, especially during summer and spring. In addition, there is a notable increase in the number of days above the defined UTCI thresholds for each season. Average annual UTCIs values also show a positive, rising trend, ranging from 0.50 °C to 1.33 °C. The most significant deviations from the average UTCI values, both seasonal and annual, were recorded in 2000, 2007, 2012, 2015, 2017, and 2018.

Dynamic multi-sensor platform for efficient 3D-digitalization of cities: part II

2021 ◽  
pp. 1-9
Author(s):  
Angel-Ivan Garcia-Moreno
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Data Set ◽  
Urban Scenes ◽  
3D Data ◽  
Sensor Platform ◽  
Laser Scanners ◽  
Potential Applications

Abstract The digitization of geographic environments, such as cities and archaeological sites, is of priority interest to the scientific community due to its potential applications. But there are still several issues to address. There are various digitization strategies, which include terrestrial/ airborne platforms and composed of various sensors, among the most common, cameras and laser scanners. A comprehensive methodology is presented to reconstruct urban environments using a mobile land platform. All the implemented stages are described, which includes the acquisition, processing, and correlation of the data delivered by a Velodyne HDL-64E scanner, a spherical camera, GPS, and inertial systems. The process to merge several point clouds to build a large-scale map is described, as well as the generation of surfaces. Being able to render large urban areas using a low density of points but without losing the details of the structures within the urban scenes. The proposal is evaluated using several metrics, for example, Coverage and Root-Mean-Square-Error (RSME). The results are compared against 3 methodologies reported in the literature. Obtaining better results in the 2D/3D data fusion process and the generation of surfaces. The described method has a low RMSE (0.79) compared to the other methods and a runtime of approximately 40 seconds to process each data set (point cloud, panoramic image, and inertial data). In general, the proposed methodology shows a more homogeneous density distribution without losing the details, that is, it conserves the spatial distribution of the points, but with fewer data.

scholarly journals 1 year, 1000 km: The Oxford RobotCar dataset

2016 ◽  
Vol 36 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Will Maddern ◽  
Geoffrey Pascoe ◽  
Chris Linegar ◽  
Paul Newman
Keyword(s):  
Autonomous Vehicles ◽  
Ground Truth ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Ground Truth Data ◽  
Full Dataset ◽  
Localization And Mapping

We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000 km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth. Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection. By frequently traversing the same route over the period of a year we enable research investigating long-term localization and mapping for autonomous vehicles in real-world, dynamic urban environments. The full dataset is available for download at: http://robotcar-dataset.robots.ox.ac.uk

scholarly journals A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Rendong Wang ◽  
Youchun Xu ◽  
Miguel Angel Sotelo ◽  
Yulin Ma ◽  
Thompson Sarkodie-Gyan ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Initial Position ◽  
Urban Environments ◽  
Data Set ◽  
Registration Method ◽  
Position Errors ◽  
Initial Errors ◽  
High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

scholarly journals Temperature Measurements on a Solar and Low Enthalpy Geothermal Open-Air Asphalt Surface Platform in a Cold Climate Region

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 979 ◽  
Author(s):  
Caner Çuhac ◽  
Anne Mäkiranta ◽  
Petri Välisuo ◽  
Erkki Hiltunen ◽  
Mohammed Elmusrati
Keyword(s):  
Heat Flux ◽  
Urban Areas ◽  
Storage System ◽  
Energy Resource ◽  
Cold Climate ◽  
Solar Irradiation ◽  
Solar Heat ◽  
Heat Collector ◽  
One Year

Solar heat, already captured by vast asphalt fields in urban areas, is potentially a huge energy resource. The vertical soil temperature profile, i.e., low enthalpy geothermal energy, reveals how efficiently the irradiation is absorbed or radiated back to the atmosphere. Measured solar irradiation, heat flux on the asphalt surface and temperature distribution over a range of depths describe the thermal energy from an asphalt surface down to 10 m depth. In this study, those variables were studied by long-term measurements in an open-air platform in Finland. To compensate the nighttime heat loss, the accumulated heat on the surface should be harvested during the sunny daytime periods. A cumulative heat flux over one year from asphalt to the ground was 70% of the cumulative solar irradiance measured during the same period. However, due to the nighttime heat losses, the net heat flux during 5 day period was only 18% of the irradiance in spring, and was negative during autumn, when the soil was cooling. These preliminary results indicate that certain adaptive heat transfer and storage mechanisms are needed to minimize the loss and turn the asphalt layer into an efficient solar heat collector connected with a seasonal storage system.

scholarly journals Geochemical perspectives from a new aerosol chemical mass closure

2007 ◽  
Vol 7 (6) ◽  
pp. 1657-1670 ◽  
Author(s):  
B. Guinot ◽  
H. Cachier ◽  
K. Oikonomou
Keyword(s):  
Urban Areas ◽  
Conversion Factor ◽  
Major Ions ◽  
Fine Fraction ◽  
Coarse Fraction ◽  
Urban Environments ◽  
Data Set ◽  
K Value ◽  
Mass Closure ◽  
Chemical Mass Closure

Abstract. The aerosol chemical mass closure is revisited and a simple and inexpensive methodology is proposed. This methodology relies on data obtained for aerosol mass, and concentration of the major ions and the two main carbon components, the organic carbon (OC) and the black carbon (BC). Atmospheric particles are separated into coarse (AD>2 μm) and fine (AD<2 μm) fractions and are treated separately. For the coarse fraction the carbonaceous component is minor and assumption is made for the conversion factor k of OC-to-POM (Particulate Organic Matter) which is fixed to the value of 1.8 accounting for secondary species. The coarse soluble calcium is shown to display a correlation (regression coefficient f, y axis intercept b) with the missing mass. Conversely, the fine fraction is dominated by organic species and assumption is made for dust which is assumed to have the same f factor as the coarse mode dust. The fine mode mass obtained from chemical analyses is then adjusted to the actual weighed mass by tuning the k conversion factor. The k coefficient is kept different in the two modes due to the expected different origins of the organic particles. Using the f and k coefficient obtained from the data set, the mass closure is reached for each individual sample with an undetermined fraction less than 10%. The procedure has been applied to different urban and peri-urban environments in Europe and in Beijing and its efficiency and uncertainties on f and k values are discussed. The f and k coefficients are shown to offer consistent geochemical indications on aerosol origin and transformations. f allows to retrieve dust mass and its value accounting for Ca abundance in dust at the site of investigation may serve as an indicator of dust origin and aerosol interactions with anthropogenic acids. f values were found to vary in the 0.08–0.12 range in European urban areas, and a broader range in Beijing (0.01–0.16). As expected, k appears to be a relevant proxy for particle origin and ageing and varies in the 1.4–1.8 range. For Beijing, k exhibits high values of about 1.7 in winter and summer. Winter values suggest that fresh coal aerosol might be responsible for such a high k value, which was not taken into account in previous works.

scholarly journals THE COMBINATION OF THE IMAGE AND RANGE-BASED 3D ACQUISITION IN ARCHAEOLOGICAL AND ARCHITECTURAL RESEARCH IN THE ROYAL CASTLE IN WARSAW

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 177-184
Author(s):  
A. Bocheńska ◽  
J. Markiewicz ◽  
S. Łapiński
Keyword(s):  
Laser Scanning ◽  
Retaining Wall ◽  
Weather Conditions ◽  
Point Clouds ◽  
Sensor Data ◽  
Data Sets ◽  
Multi Temporal ◽  
Close Range ◽  
3D Acquisition ◽  
Architectural Research

<p><strong>Abstract.</strong> The paper presents archaeological and architectural research in the Royal Castle in Warsaw where a combination of image- and range-based 3D acquisition was applied. The area examined included excavations situated inside the Tower and near its outer western wall. The work was carried out at various periods and in different weather conditions. As part of the measurements, laser scanning was performed (with a Z+F 5006h scanner) and a series of close-range images were taken. It was important to integrate the data acquired to create a comprehensive documentation of archaeological excavations. When data was acquired from TLS together with photogrammetric data (in different measurement periods), the points' displacements were controlled and analysed. The process of orienting and processing the terrestrial images included photographs taken during the inventory of the tower (Canon 5D Mark II) and photographs provided by the Castle's employees (Canon PowerShot G5 X). Agisoft PhotoScan software was used to orient and process the terrestrial images, and LupoScan for the TLS data. In order to integrate the TLS data and the clouds of points from the photographs from the various stages, they were processed into a raster form; our own software (based on the OpenCV library and the Structure-from-Motion method) and LupoScan software were used to interconnect the multi-temporal and multi-sensor data sets. As a result of processing photographs and TLS data, point clouds in an external reference system were obtained. This data was then used to study the thickness of the walls of the Justice Court Tower, to analyse the course of the retaining wall, and to generate the orthoimages necessary for chronological analysis.</p>

scholarly journals A VOXEL-BASED METADATA STRUCTURE FOR CHANGE DETECTION IN POINT CLOUDS OF LARGE-SCALE URBAN AREAS

2018 ◽  
Vol IV-2 ◽  
pp. 97-104
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Detection Methods ◽  
Planar Surfaces ◽  
Occupancy Grids ◽  
Speed Up

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

scholarly journals Sociodemographic and clinical characteristics, causal factors and evolution of a group of patients with chronic urticaria-angioedema

2007 ◽  
Vol 125 (5) ◽  
pp. 281-285 ◽  
Author(s):  
Maria Regina Cavariani Silvares ◽  
Kunie Iabuki Rabello Coelho ◽  
Ivete Dalben ◽  
Joel Carlos Lastória ◽  
Luciana Patrícia Fernandes Abbade
Keyword(s):  
Chronic Urticaria ◽  
Urban Areas ◽  
Clinical Characteristics ◽  
Laboratory Data ◽  
Dermatological Condition ◽  
One Year ◽  
De Medicina ◽  
Clinical Worsening

CONTEXT AND OBJECTIVE: Chronic urticaria-angioedema is a common, multiple-cause complaint. The aim was to investigate the sociodemographic and clinical characteristics, causal and aggravating factors and evolution of urticaria-angioedema. DESIGN AND SETTING: This was a descriptive prospective study carried out at the Dermatology outpatient clinic of Faculdade de Medicina de Botucatu, Universidade Estadual Paulista (Unesp). METHODS: A total of 125 patients with chronic urticaria-angioedema were evaluated to obtain sociodemographic data, anamnesis, dermatological and general clinical data and laboratory data, emphasizing causal and aggravating factors and complaint evolution. RESULTS: Chronic urticaria-angioedema occurred mainly in females (mean age: 35 years), but also in men (mean age: 32 years). White color and living in urban areas also predominated. There was no preferential time for symptoms to appear, and nighttime was the most commonly reported time for clinical worsening. Around half of the patients had urticaria associated with angioedema. There were no associated factors in most of the cases, and stress was the most commonly reported aggravating factor. The cause was ascertained in 37.6% of our cases. The mean duration of follow-up was 11.7 months. Around 60% of the patients evolved with the problem under control, 32% improved, 9% had no change in dermatological condition and only one patient worsened. CONCLUSIONS: Chronic urticaria-angioedema was more common among middle-aged women. It is a long-term disease, and its cause was explained in about one-third of the patients. Half of the patients presented disease control after treatment lasting an average of approximately one year.

scholarly journals The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps

2019 ◽  
Vol 8 (3) ◽  
pp. 116 ◽  
Author(s):  
Cláudia M. Viana ◽  
Luis Encalada ◽  
Jorge Rocha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rural Areas ◽  
Urban Areas ◽  
Coverage Area ◽  
Training Samples ◽  
Spatial Coverage ◽  
Multi Temporal ◽  
One Year ◽  
Thematic Accuracy

OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features’ boundaries. Features with larger areas (> 10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries

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