Mapping and Localization for Robots

1. Mapping and Localization for Robots The Occupancy Grid Approach

2. Agenda • Introduction • Mapping • Occupancy grids • Sonar Sensor Model • Dynamically Expanding Occupancy Grids • Localization • Iconic • Feature-based • Monte Carlo

3. An intelligent robot is a mechanical creature which can function autonomously. • Intelligent – the robot does not do things in a mindless, repetitive way. • Function autonomously – the robot can operate in a self-contained manner, under reasonable conditions, without interference by a human operator.

4. Robots in museums

5. Personal Robots

6. Robots in space

7. The problem of Navigation • Where am I going? • What’s the best way there? • Where have I been? • Where am I? • How am I going to get there?

8. Mapping • Topological Mapping • Features and Landmarks • Milestones with connections • Hard to scale • Metric Mapping • Geometric representations • Occupancy Grids • Larger maps much more computationally intensive

9. Map Making

10. Demo of Mapping • The Littlejohn Project • http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/mercator/www/ littlejohn/

11. Occupancy Grids • A tool to construct an internal model of static environments based on sensor data. • The environment to be mapped is divided into regions. • Each grid cell is an element and represents an area of the environment.

12. Representation of Occupancy Grids

13. Sonar Sensor Model

14. Methods of Sonar Reading Probabilistic Methods: • Bayesian • Dempster-Shafer • HIMM (Histogrammic In Motion Mapping)

15. Why Probabilistic Mapping? • Noise in commands and sensors • Commands are not executed exactly (eg. Slippage leads to odometry errors) • Sonars have several error issues(eg. cross-talk, foreshortening, specular reflection)

16. Occupancy Grids • Pros • Simple • Accurate • Cons • Require fixed-size environment:difficult to update if size of mapped area changes.

17. Dynamically Expanding Occupancy Grids • Variable-sized maps • Ability to increase size of map, if new areas are added to the environment • Start mapping at center of nine-block grid • As robot explores, new cells are added • Global map is stored outside the RAM in a file or a database

18. Representation of DEOGs

19. Adding Cells to a DEOG

21. Dynamically Expanding Occupancy Grids • Best (the only?) solution for mapping changing environments. • Saves RAM • Other useful information can be stored in the map • More complicated to program than regular occupancy grids

22. Localization Where am I? Methods: • Iconic • Feature-based • Monte-Carlo

23. Iconic Localization • Use raw sensor data • Uses occupancy grids • Current map is compared with original map. If original map has errors, localization is very inaccurate. • Localization errors accumulate over time.

24. The Concept • “pose”: (x, y, θ)location, orientation • Compare small local occupancy grid with stored global occupancy grid. • Best fit pose is correct pose.

25. Feature-based Localization • Compares currently extracted features with features marked in a map. • Requires presence of easily extractable features in the environment. • If features are not easily distinguishable, may mistake one for the other.

26. Monte Carlo Localization • Probabilistic • 1. Start with a uniform distribution of possible poses (x, y, ) • 2. Compute the probability of each pose given current sensor data and a map • 3. Normalize probabilities • Throw out low probability points • Performance • Excellent in mapped environments • Need non-symmetric geometries

27. References: • Introduction to AI RoboticsDr. Robin Murphy • Dynamically Expanding Occupancy GridsBharani K. Ellore • Multi-agent mapping using dynamic allocation utilizing a storage systemLaura Barnes, Richard Garcia, Todd Quasny, Dr. Larry Pyeatt • Robotic Mapping: A surveySebastian Thrun • Littlejohn Projecthttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/mercator/www/littlejohn/ • CYE www.prorobotics.com • The Honda Asimo http://asimo.honda.com • Mars Rover http://marsrovers.jpl.nasa.gov/home/