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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This paper describes a calibration method for inertial and magnetic sensors using a batched optimization procedure. Well estab- lished sensor, motion and constraint models are applied which include sensor gains, biases, misalignments and inter-triad misalignments. For the magnetometer, hard and soft iron model parameters and local dip- angle are embodied in the framework as well. The method does not require any additional equipment, is minimal restrictive with respect to the required movements, and can be performed within one minute. Our approach is applicable for both single and multi Inertial Measurement Units (IMU) and leverages from the relative pose between rigidly con- nected IMU’s. We demonstrated that our approach resulted in improved dead reckoning estimates and showed good agreements with an optical reference system for both position and orientation estimates.
MULTIFILE
To better control the growing process of horticulture plants greenhouse growers need an automated way to efficiently and effectively find where diseases are spreading.The HiPerGreen project has done research in using an autonomous quadcopter for this scouting. In order for the quadcopter to be able to scout autonomously accurate location data is needed. Several different methods of obtaining location data have been investigated in prior research. In this research a relative sensor based on optical flow is looked into as a method of stabilizing an absolute measurement based on trilateration. For the optical flow sensor a novel block matching algorithm was developed. Simulated testing showed that Kalman Filter based sensor fusion of both measurements worked to reduce the standard deviation of the absolute measurement from 30 cm to less than 1 cm, while drift due to dead-reckoning was reduced to a maximum of 11 cm from over 36 cm.
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De docent/onderzoeker rol is de belangrijkste, echter ook minst goed gefaciliteerde, rol binnen de hogeschool. De docent/onderzoeker moet continue schakelen tussen de onderwijs-urgentie (teamleider) en de langere termijn onderzoeksprioriteit (lector). De docent/onderzoeker heeft praktisch gezien twee werkgevers. Het RAAK-Postdoc project HENC beoogd een pragmatische grondlegger te ontwikkelen voor de duurzame inbedding van PhDs in deze docent/onderzoeker rol. Henk Kortier fungeert hierbij als initiator, (mede) ontwikkelaar en eerste (proef-)persoon. Het onderzoek dat onderdeel vormt van deze aanvraag beoogt de valorisatie van het op 09-feb-2018 afgesloten biomedisch wetenschappelijk PhD onderzoek van Henk Kortier. De modulaire robotica technieken die Henk gaat door ontwikkelen hebben spin-off naar de drie Saxion onderzoek domeinen Area’s & Living (drones), Smart Industry (grondrobots) en Health & Wellbeing (opruimrobot). De onderwijsactiviteiten richten zich op een, nieuw te ontwikkelen, module binnen de opleiding mechatronica, met als doel concrete invulling te geven aan de noodzakelijke vernieuwing en integratie van onderzoek en onderwijs. Met het onderwijs en onderzoeksteam van mechatronica is hierover op 23 april jl. een inventarisatie workshop gehouden, ondersteund door de teamleider onderwijs en lector. Door een matrix-analyse zijn de belangrijkste punten gedefinieerd en worden de belangrijkste redenen voor PhD om als docent/onderzoeker te blijven fungeren ontwikkeld, getest, uitgevoerd en uitgerold. Op deze wijze geeft het project concreet invulling aan het Saxion beleid om PhDs te kunnen laten werken aan het onderzoek en via onderwijsvernieuwing de resultaten naar onderwijs vloeien. Naast de onderwijs-onderzoeks integratie component wordt er binnen de module een lespakket ontwikkeld ter behoeve van het autonoom functionerende robots. Dit pakket wordt ontwikkeld vanuit zowel een operator als engineering oogpunt en zal derhalve de opleiding mechatronica overstijgen. Dit maakt het pakket breed inzetbaar binnen de verschillende opleidingen van de academie Life Science, engineering and Design en Creative Technologievan Saxion maar ook voor hogescholen elders.
Automating logistics/agrifood vehicles requires dependable, accurate positioning. Automated vehicles, or mobile robots, constantly need to know their exact position to follow the trajectories required to perform their tasks. Precise outdoor localization is helped by the increased price/performance ratio of RTK-GNSS solutions. However, this technology is sensitive to signal deterioration by e.g. biomass and large structures like poles/buildings. Robust localization requires additional localization technologies. Several absolute and relative positioning technologies exist and available sensor fusion solutions allow for combining these technologies. However, robot developers require modularity, and no integral solutions exist. Commercial solutions are either customized or high-priced testing solutions. Academics mainly propose specific sensing combinations and lack industrial applicability. Market demand articulation expresses the need for redundancy besides modularity, both for vehicle safety and system resilience, referring to the current geopolitical GPS jamming reality. MAPS aims for an open-source, ROS2-based, multi-modal, robust and modular localization solution for outdoor logistics and agrifood applications, enabling dependable and safe vehicle automation, allowing both sectors to handle labor shortages, introduce durable solutions and enhance resilience. MAPS focuses on a sensor fusion approach allowing modularity, with integrated redundancy. It includes online confidence level estimation, supporting both continuous fusion and modality switching, aiming for location/situation aware behavior and allowing for market-requested hybrid in-vehicle/infra solutions. MAPS intents to maximally utilize the consortium’s vehicle dynamics knowledge - including vehicle-(soft)soil interaction - in the solution for plausibility and dead reckoning. An accompanying PhD/EngD research is foreseen. With project partners enabling scalable, industry-grade solutions MAPS aims to bridge the gap between academic-level research and market-desired applicability. MAPS is independent, though aims to cooperate with AIFusIOn from Saxion on re-usable architectures and integration of AIFusIOn specifics, like AI-based situational awareness and indoor-outdoor switching, if both are granted.
