Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
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The importance of specific professions for human rights realization is increasingly recognized. Journalists, teachers, and civil servants are all considered to play a role because their work affects individual rights. This is also the case for social workers. The connection between social work and human rights is evident in the large amount of literature explaining how human rights relate to social work. At the same time there is more attention for human rights localization. These fields of knowledge are related: social workers are local professionals and if they start applying human rights in their work this may influence human rights localization. This article contributes to existing debates on human rights localization by reflecting on the potential role of social workers in local human rights efforts in the Netherlands. Since human rights localization in general and human rights application in social work are recent phenomena in the Netherlands this provides a useful case study for a qualitative analysis on whether and how social workers can be regarded as actors in human rights localization. By connecting different actors that are said to play a role in human rights localization to proposed forms of human rights application by social workers this article identifies three possible roles for social workers in human rights localization: as human rights translators, as human rights advocates, and as human rights practitioners.
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Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
