We present an approach to vision-based mobile robot localisation. That is, the task of obtaining a precise position estimate for a robot in a previously explored environment, even without an a priori estimate. Our approach combines the strengths of statistical and feature-based methods. This is accomplished by learning a set of visual features called landmarks, each of which is detected as a local extremum of a measure of uniqueness and represented by an appearance-based encoding. Localisation is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Experimental evidence shows that an estimate accurate to a fraction of the environment sampling density can be obtained for a wide range of parameterisations, even under scaling of the explored region, and changes in sampling density.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.20863 |
| Date | January 1998 |
| Creators | Sim, Robert. |
| Contributors | Dudek, Gregory (advisor) |
| 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: 001641733, proquestno: MQ44278, Theses scanned by UMI/ProQuest. |
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