Lecture 07: Localization and Mapping I
- 1. Introduction to RoboticsLocalization and Mapping IMarch 1, 2010
- 2. Last week’s exerciseRobotStadiumIntroduction to SVN (versioning/collaboration tool)TasksLocomotion: need to get up and runPerception: need to orient itselfCommunicate: need to share informationLocalize: need to reason about spaceDeliberate: need to plan what to do nextShare the load: 1 or 2 tasks per studentPresent your plan in 2 weeks in class – be specific
- 3. Localization
- 4. LocalizationGyroscopeOdometryControl inputGPSLandmarksSensor input with different uncertainties. What is the overall uncertainty of the estimate?
- 5. Uncertainty Models: The Gaussian Distribution
- 6. Error PropagationIntuition: the more sensitive the estimated quantity is to perception error, the more this sensor should be weightedCovariance matrixRepresenting outputuncertaintiesFunction relating sensor inputto output quantitiesCovariance matrixrepresenting inputuncertainties
- 7. Differential Wheel Robot Odometry
- 10. Belief representationParametric, single hypothesisParametric, multi hypothesisNon-parametric, multi hypothesis(particle filter)
- 12. Example: Google MapsContinuous, Discrete or Topological?
- 13. Belief representation in topological maps
- 15. From Sensor Data to Topological MapsExact Decomposition
- 16. Voronoi DecompositionPoints on lines have the same distance to neighboring obstaclesVoronoi edges correspond to the safest path
- 18. Exercise: Navigation AlgorithmsFind the shortest path from A to BChoose the map representationDevise an algorithm to extract path
- 19. Reactive vs. Deliberative PlanningSo farMove randomlyUse heuristics (follow wall, spiral, …)Use landmarks (infrared beacons, magnet wire)Use gradients / feedback control (Exercise 2)TodayDeliberative planningReason on abstract representation
- 20. HomeworkSection 5.6 (pages 212-244)