Abstract
The tourism sector has been facing continuous growth. It plays a vital role in countries' economic development, highlighting the need to keep nurturing it by making it easier and more attractive. This paper presents Tourism as a Service - an innovative concept that aims to ease a day in the life of a tourist by integrating services that might be found spread out through separate tools and services, including ticketing in public transport and touristic attractions, route planning, information, among others. First, focus groups were done in order to understand the users' needs regarding the use of a mobile ticketing solution in tourism. The findings from the literature reviewed and the previous step were then prioritized by relevance in a questionnaire sent to potential users, allowing the creation of a medium-fidelity prototype. The validation through usability testing confirmed an interest in the proposed solution. The critical design choices surrounding the proposed solution were discussed along with improvements and further work to be done.

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Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.