Abstract

Light Detection and Ranging, as an active Remote Sensing Technology, enables gathering accurate, three-dimensional point cloud of scanned objects. Laser scanning might be provided on the terrestrial level for specific, defined constructions, as well as on the airborne level for aerial or linear objects. Using a laser sensor mounted on a moving platform is currently the most efficient way of obtaining in a short period, accurate positions of billions of points as a representation of a scanned area. Based on this kind of dataset it is possible to perform three-dimensional analysis of the safety of scanned objects without additional measurements in the field. This article presents the analysis performed in vMatic software on data from Airborne Laser Scanning for medium voltage power line verification of obstacles with the buildings. The analysis took less than 20 seconds for the detection of buildings points that are closer than 5m from conductors for seven spans wit a total length of almost 400m. Providing distance verification on 3D point cloud data is the fastest way to obtain a hazard awareness in a short time. Once acquired by LiDAR data can be used for other various analyses for any construction, depending on current, expected and future needs.

Abstract

This article focuses on testing and investigating further development needs for LiDARs in self-driving cars in adverse weather. The article compares two different LiDARs (Ibeo Lux and Velodyne PUCK), which both use the 905 nm wavelengths, which are used in more than 95% of currently available LiDARs. The performance was tested and estimated in stabilized fog conditions at Cerema fog chamber facilities. This provides a good basis for repeating the same validation procedure multiple times and ensuring the right development decisions. However, performance of the LiDARs suffers when the weather conditions become adverse and visibility range decreases. A 50% reduction in target detection performance was observed over the exhaustive tests. Therefore, changing to higher wavelengths (1550 nm) was considered using redesigned 'pre-prototype LiDAR'. The preliminary results indicate that there is no reason to not use 1550 nm wavelength, which due to eye safety regulations gives an opportunity to use 20 times more power compared to the traditional 905 nm. In order to clarify the expected benefits, additional feasibility studies are still needed.

Abstract

This article aims to outline the key results of testing and encountered challenges of various LiDARs, radar and stereo camera in arctic weather conditions. The test session was conducted in two different urban areas in Finland in the middle of January 2019. The arctic conditions turned out to be challenging for the sensors dedicated more to areas where temperature stays relatively warm. The aim of this one-week test session was to assess performance deterioration when powdered snow, salted road, snowy ground and sun light influence reliability of the future automated driving functions. This study focuses mainly on the issues with hardware that are basic building blocks for the situation awareness software modules. Furthermore, the countermeasures such as protecting sensors and mounting positions have been proposed.The test results indicate that some sensors significantly lose performance when temperature drops to less than -10 degrees centigrade. The problem is not merely mechanical freezing of the spinning LiDAR components but properties of laser illumination may change due to temperature variation, too. Since LiDAR is an optical device, they also suffer when there is turbulent snow in front of the sensor. The turbulence looks like a noise and partially blocks the laser echoes from surrounding environment. The performance, measured by laser pulse echo count, can with some sensors drop more than 50 percent. This seriously diminishes the sensing range and furthermore, makes pattern recognition unreliable. The two other sensor types which were taken into account are stereo vision and radar. They have a role in automated driving to compensate performance degradation of LiDARs due to arctic conditions.

Abstract

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by today's maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicle's position. Document type: Part of book or chapter of book

Abstract

ksandrs Urbahs  / Rima Mickevičienė / Vasilij Djačkov / Kristīne Carjova / Valdas Jankūnas / Mindaugas Zakarauskas / Natalia Panova / Dita Lasmane Published Online: 2017-03-14 | DOI: https://doi.org/10.1515/tae-2016-0012 Abstract The paper gives brief description of the conventional and innovative hydrography survey methods and constraints connected with the realization. Proposed hydrographic survey system based on the use of Unmanned Aerial and Maritime systems provides functionality to conduct hydrographic measurements and environment monitoring. System can be easily adapted to fulfil marine safety and security operations, e.g. intrusion threat monitoring, hazardous pollutions monitoring and prevention operations, icing conditions monitoring. Document type: Article

Abstract

This paper presents and discusses the recent developments related to seismic performance and assessment of buried pipelines. The experience from the performance of pipelines during last earthquakes provided invaluable information and lead to new developments in the analysis and technologies. Especially, the pipeline performance during Canterbury earthquake sequence in New Zealand is taken as a case study here. The data collected for the earthquake sequence are unprecedented in size and detail, involving ground motion recordings from scores of seismograph stations, high resolution light detection and ranging (LiDAR) measurements of vertical and lateral movements after each event, and detailed repair records for thousands of km of underground pipelines with coordinates for the location of each repair. One of the important learnings from the recent earthquakes is that some earthquake resistant design and technologies proved to be working. This provides a motivation to increase international exchange and cooperation on earthquake resistant technologies. Another observation is that preventive maintenance is important to reduce the pipeline damage risk from seismic and other hazards. To increase the applicability and sustainability, seismic improvements should be incorporated into the pipe replacement and asset management programs as part of the preventive maintenance concept. However, it is also important to put in the most proper pipeline from the start as replacing or retrofitting the pipelines later requires substantial investment. In this respect, seismic considerations should be taken into account properly in the design phase. © The Author(s) 2015. Document type: Part of book or chapter of book

Abstract

ITT Industries Space Systems Division (Space Systems) has developed an airborne natural gas leak detection system designed to detect, image, quantify, and precisely locate leaks from natural gas transmission pipelines. This system is called the Airborne Natural Gas Emission Lidar (ANGEL) system. The ANGEL system uses a highly sensitive differential absorption Lidar technology to remotely detect pipeline leaks. The ANGEL System is operated from a fixed wing aircraft and includes automatic scanning, pointing system, and pilot guidance systems. During a pipeline inspection, the ANGEL system aircraft flies at an elevation of 1000 feet above the ground at speeds of between 100 and 150 mph. Under this contract with DOE/NETL, Space Systems was funded to integrate the ANGEL sensor into a test aircraft and conduct a series of flight tests over a variety of test targets including simulated natural gas pipeline leaks. Following early tests in upstate New York in the summer of 2004, the ANGEL system was deployed to Casper, Wyoming to participate in a set of DOE-sponsored field tests at the Rocky Mountain Oilfield Testing Center (RMOTC). At RMOTC the Space Systems team completed integration of the system and flew an operational system for the first time. The ANGELmore » system flew 2 missions/day for the duration for the 5-day test. Over the course of the week the ANGEL System detected leaks ranging from 100 to 5,000 scfh.« le Document type: Report

Abstract

Cirrus clouds have a large influence on the Earth's climate and anthropogenic activities such as aviation can alter their properties. Besides the formation of contrails, indirect effects on naturally occurring cirrus like increased heterogeneous freezing due to exhaust soot particles are discussed in the literature. However, hardly any observational study exists. In this work we present cirrus optical properties measured by an airborne lidar over Europe during the Midlatitude Cirrus experiment (ML-CIRRUS). One half of the cloud cases showed elevated depolarization ratios with a mode difference of 10 percentage points indicating differences in the clouds microphysical properties. Their origin can be traced back to highly frequented air traffic regions, and they show lower in-cloud ice supersaturations. Our analysis reveals no influence of embedded contrails and temperature. These results could be explained by an indirect aerosol effect where heterogeneous freezing is caused by aviation exhaust particles.

Abstract

Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance. 

Abstract

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system. Document type: Article