Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-Rand RTI2018-096036-B-C21), Universidad Carlos III of Madrid through (PEAVAUTO-CM-UC3M) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362)

Roles and Self-Reconfigurable Robots

Abstract. A self-reconfigurable robot is a robotic device that can change its own shape. Self-reconfigurable robots are commonly built from multiple identical modules that can manipulate each other to change the shape of the robot. The robot can also perform tasks such as locomotion without changing shape. Programming a modular, self-reconfigurable robot is however a complicated task: the robot is essentially a real-time, distributed embedded system, where control and communication paths often are tightly coupled to the current physical configuration of the robot. To facilitate the task of programming modular, self-reconfigurable robots, we have developed a declarative, role-based language that allows the programmer to associate roles and behavior to structural elements in a modular robot. Based on the role declarations, a dedicated middleware for high-level distributed communication is generated, significantly simplifying the task of programming self-reconfigurable robots. Our language fully supports programming the ATRON self-reconfigurable robot, and has been used to implement several controllers running both on the physical modules and in simulation

Innovative surveying methodologies through Handheld Terrestrial LIDAR Scanner technologies for forest resource assessment

Precision Forestry is an innovative sector that is currently of great importance for forest and spatial planning. It enables complex analyses of forest data to be carried out in a simple and economical way and facilitates collaboration between technicians, industry operators and stakeholders, thus ensuring transparency in forestry interventions (Corona et al., 2017). The principles of "Precision Forestry" are to use modern tools and technologies with the aim to obtain as much real information as possible, to improve decision-making, and to ensure the current objectives of forest management. Thanks to the rapid technological developments in remote sensing during the last few decades, there have been remarkable improvements in measurement accuracy, and consequentially improvements in the quality of technical elaborations supporting planning decisions. During this period, several scientific publications have demonstrated the potential of the LIDAR system for measuring and mapping forests, geology, and topography in large-scale forest areas. The LIDAR scans obtained from the TLS and HLS systems provide detailed information about the internal characteristics of tree canopys, making them an essential tool for studying stem allometry, volume, light environments, photosynthesis, and production models. In light of these considerations, this thesis aims to expand the current knowledge on the terrestrial LIDAR system applications for monitoring forest ecosystems and dynamics by providing insight on the feasibility and effectiveness of these systems for forest planning. In particular, this study fills a gap in the literature regarding practical examples of the use of innovative technologies in forestry. The main themes of this work are: A) The strengths and weaknesses of the mobile LIDAR system for a forest company; B) The applicability and versatility of the LIDAR HLS tool for sustainable forest management applications; C) Single tree analysis from HLS LIDAR data.   To investigate these themes, we analyzed six cases studies: 1) An investigation of the feasibility and efficiency of LIDAR HLS scanning for an accurate estimation of forest structural attributes by comparing scans using the LIDAR HLS survey method (Handheld Mobile Laser Scanner) to traditional instruments; 2) An examination of walking scan path density’s influence on single-tree attribute estimation by HMLS, taking into account the structural biodiversity of two forest ecosystems under examination, and an estimation of the cost-effectiveness of each type of laser survey based on the path scheme considered; 3) A study of how LIDAR HLS surveys can contribute to fire prevention interventions by providing a quantitative classification of fuels and a preliminary description of the structural and spatial development of the forest in question; 4) An application of a method for assessing and rating stem straightness in tree posture using LIDAR HLS surveys to quantify differences between stands of different log qualities; 5) The identification of features of a Mediterranean old-growth forest using LIDAR HLS surveys according to the criteria established in the literature; 6) The extrapolation of dimensional information for Ficus macrophylla subsp. columnaris to identify the monumental character of the tree by comparing the most appropriate LIDAR HLS point cloud processing methodologies and estimating the total volume of individual trees. In conclusion, the results of these cases studies are useful to determine new research aspects within the system in the forest environment by applying recently published analysis methodologies and indications of relevant terrestrial LIDAR methodologies

WHERE DO YOU LOOK? RELATING VISUAL ATTENTION TO LEARNING OUTCOMES AND URL PARSING

Visual behavior provides a dynamic trail of where attention is directed. It is considered the behavioral interface between engagement and gaining information, and researchers have used it for several decades to study user\u27s behavior. This thesis focuses on employing visual attention to understand user\u27s behavior in two contexts: 3D learning and gauging URL safety. Such understanding is valuable for improving interactive tools and interface designs. In the first chapter, we present results from studying learners\u27 visual behavior while engaging with tangible and virtual 3D representations of objects. This is a replication of a recent study, and we extended it using eye tracking. By analyzing the visual behavior, we confirmed the original study results and added more quantitative explanations for the corresponding learning outcomes. Among other things, our results indicated that the users allocate similar visual attention while analyzing virtual and tangible learning material. In the next chapter, we present a user study\u27s outcomes wherein participants are instructed to classify a set of URLs wearing an eye tracker. Much effort is spent on teaching users how to detect malicious URLs. There has been significantly less focus on understanding exactly how and why users routinely fail to vet URLs properly. This user study aims to fill the void by shedding light on the underlying processes that users employ to gauge the UR L\u27s trustworthiness at the time of scanning. Our findings suggest that users have a cap on the amount of cognitive resources they are willing to expend on vetting a URL. Also, they tend to believe that the presence of www in the domain name indicates that the URL is safe

Monocular SLAM for Visual Odometry: A Full Approach to the Delayed Inverse-Depth Feature Initialization Method

This paper describes in a detailed manner a method to implement a simultaneous localization and mapping (SLAM) system based on monocular vision for applications of visual odometry, appearance-based sensing, and emulation of range-bearing measurements. SLAM techniques are required to operate mobile robots in a priori unknown environments using only on-board sensors to simultaneously build a map of their surroundings; this map will be needed for the robot to track its position. In this context, the 6-DOF (degree of freedom) monocular camera case (monocular SLAM) possibly represents the harder variant of SLAM. In monocular SLAM, a single camera, which is freely moving through its environment, represents the sole sensory input to the system. The method proposed in this paper is based on a technique called delayed inverse-depth feature initialization, which is intended to initialize new visual features on the system. In this work, detailed formulation, extended discussions, and experiments with real data are presented in order to validate and to show the performance of the proposal

DLR Lampoldschausen combines its successful space focus with forward-looking energy research

Energy-intensive applications are both the origin and future of hydrogen research, which has been an integral part of spaceflight for several decades. Researchers at the DLR Institute of Space Propulsion in Lampoldshausen are modelling the entire process, from the generation of renewable hydrogen to storage and liquefaction, all the way through to the testing of hydrogen in rocket propulsion systems. This work is making a major contribution towards achieving future sustainable spaceflight; meanwhile, the knowledge gained in working with hydrogen is being transferred to sectors including energy, mobility, air transport and shipping. Furthermore, this work is also kindly acknowledged by Hydrogen Europe Research, the leading European grouping on Fuel Cells and hydrogen technologies