3D Maps Representation Using GNG

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

Urban Planning 3. Informal notes from the course's theoretical and practical content

This document compiles in an informal manner, some of the theoretical teaching content considered for the Urban Planning 3, course 2013- 2014. It also includes the outline and hand-in instructions of the two practical exercises developed during the course. The purpose of this compilation of teaching notes is to serve as reference and basis for future courses taught in English, as this was the first experience for the subject Urban Planning 3.