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
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
• 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
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
• 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
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.
You've reached the end of your free preview.
Want to read all 20 pages?
- Fall '19