Robots carrying tasks in an unknown environment often need to build a map in order to be able to navigate. One approach is to create a detailed map of the environment containing the position of obstacles. But this option can use a large amount of memory, especially if the environment is large. Another approach, closer to how people build a mental map, is the topological map. A topological map contains only places that are easy to recognize (landmarks) and links them together. / In this thesis, we explore the issue of creating a topological map from range data. A robot in a simulated environment uses the distance from objects around it (range data) and a compass as inputs. From this information, the robot finds intersections, classifies them as landmarks using a neural network and creates a topological map of its environment. The neural network detecting landmarks is trained online on sample intersections. Although the robot evolves in a simulated environment, the ideas developed in this thesis could be applied to a real robot in an office space.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.82266 |
| Date | January 2005 |
| Creators | Künzle, Philippe |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Science (School of Computer Science.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002227306, proquestno: AAIMR12477, Theses scanned by UMI/ProQuest. |
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