Understanding how experiences unfold requires measuring participants' emotions, especially as they move from location to location. Measuring and mapping emotions over space is technically challenging, however. While a number of technologies to record and spatially resolve emotion data exist, they have not been systematically compared. We present emotion data collected at a natural and military heritage site in the Netherlands using three different methods, namely retrospective self report, experience reconstruction, and physiology. These data are applied to three corresponding mapping methods. The resulting maps lead to divergent findings, demonstrating that spatial mapping of emotion data accentuates differences between distinct dimensions of emotions.
MULTIFILE
Understanding how experiences unfold requires measuring participants' emotions, especially as they move from location to location. Measuring and mapping emotions over space is technically challenging, however. While a number of technologies to record and spatially resolve emotion data exist, they have not been systematically compared. We present emotion data collected at a natural and military heritage site in the Netherlands using three different methods, namely retrospective self report, experience reconstruction, and physiology. These data are applied to three corresponding mapping methods. The resulting maps lead to divergent findings, demonstrating that spatial mapping of emotion data accentuates differences between distinct dimensions of emotions.
MULTIFILE
This work provides a feasibility study on estimating the 3-D locations of several thousand miniaturized free-floating sensor platforms. The localization is performed on basis of sparse ultrasound range measurements between sensor platforms and without the use of beacons. We show that this task can be viewed as a specific type of pose graph optimization. The main challenge is robustly estimating an initial pose graph, that models the locations of sensor platforms. For this, we introduce a novel graph growing strategy that uses random sample consensus in alternation with non-linear refinement. The theoretical properties of our sensor cloud localization method are analyzed and its robustness is investigated using simulations. These simulations are based on inlier-outlier measurement models and focus on the application of subterranean 3-D mapping of liquid environments, such as pipe infrastructures and oil wells.
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.
