lecture5-Maps_Mapping.pdf - MAPS AND MAPPING Today \u2022 Last time we looked at localization and we assumed that we had some form of map \u2022 This time

lecture5-Maps_Mapping.pdf - MAPS AND MAPPING Today u2022...

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MAPS AND MAPPING Today Last time we looked at localization, and we assumed that we had some form of map. This time we’ll look more closely at maps and (a bit) at the process of creating maps. cisc3415-parsons-lect05 2
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Features In the first class we said that navigation begins with what the robot can “see”. There are several forms this might take, but it will depend on: What sensors the robot has What features can be extracted. These are not a particularly likely set of features. cisc3415-parsons-lect05 3 More likely features are things that can be extracted from images: Simple color segmentation. UT Austin RoboCup team. cisc3415-parsons-lect05 4
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The results of more complex image processing: Edge detection, template matching. Lanser et al (1996) cisc3415-parsons-lect05 5 One can also identify features with other kinds of sensor. Patterns of range finder readings that are identifiable. Meaning we can tell when the sensor spots them. cisc3415-parsons-lect05 6
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Map Once we have a set of features we can build a map . A map says how features sit relative to one another. cisc3415-parsons-lect05 7 Types of map Topological map Just says what the relationship between features is. cisc3415-parsons-lect05 8
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Metric topological map Provides some information on distances between features. cisc3415-parsons-lect05 9 Metric map Gives the precise location of the features. In whatever coordinate system is most appropriate. Frequently as a pose ( x , y , θ) Continuous or discrete measurements. cisc3415-parsons-lect05 10
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Cell-based maps A common way to create a map is to break up the map into a series of cells. A number of ways one might do this. As ever, there are trade-offs. Different approaches are better or worse depending on what you are trying to do. cisc3415-parsons-lect05 11 Exact cell decomposition.
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