Abstract

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by today's maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicle's position. Document type: Part of book or chapter of book

Abstract

The main goal of this paper was to assess the greenhouse gas (GHG) intensity of olive oil production in Portugal. A life-cycle model and inventory were implemented for the entire production process, including a comprehensive analysis of olive cultivation, olive oil extraction, packaging, and distribution. Data originates from five differently-sized Portuguese olive growers and from a total of six olive oil mills, representing the three extraction processes in use: three-phase extraction, two-phase extraction, and traditional pressing. The results show that the GHG intensity lies in the range 1.8-8.2 kg CO2eq/liter and that the main contributors were fertilizers (production and field emissions). Efficient use of fertilizers thus seems to be a key factor for mitigating the GHG intensity of olive oil production. This research was supported by project ECODEEP (Eco-efficiency and Eco-management in the Agro Industrial sector, FCOMP–05–0128–FEDER–018643) and the Portuguese Science