1. 1/53 Key Problems Localization –“where am I ?” Fault Detection –“what’s wrong ?” Mapping –“what is my environment like ?”

2. 2/53 Localization Combining Local and Global Information –localize with local sensing most of the time –use a map or other absolute information when available to correct Challenges –how often to use global information ? –unified framework for local and global sensing ?

3. 3/53 Localization Where is the robot –on my map ? –in a global coordinate system ? –relative to other robots ? ?

4. 4/53 Localization: Autonomous Mobile Nodes Integrate angular, linear rates of motion from onboard sensors (Durant-Whyte 91) Focus on reducing orientation error Combine a compass (or any absolute direction sensor) with a gyroscope (a rate sensor)

5. 5/53 The Kalman Filter Sensor measurements - Sensor model System model + Kalman gain X residual Scaled residual Propagate Update

6. 6/53 System Model: Robot Kinematics

7. 7/53 Filter Measurement vector Propagate Update

8. 8/53 Results 1 order of magnitude improvement using calibrated odometry and Kalman filtering compared to raw odometry (Goel, Roumeliotis and Sukhatme 99)

9. 9/53 Absolute Position Sensing (GPS) Dual estimator strategy works better than single filter with all sensor inputs (Goel, Roumeliotis and Sukhatme 99) KF2 Encoders,Gyros KF1 GPS x,y GPS available ? x,y yes no

10. 10/53 Application to Fault Detection –Multiple Kalman Filters in parallel –Each with a different model of either the sensors or the robot kinematics –Analyze residual signatures

11. 11/53 Flat Tire: Residuals vs. Time KF Nominal Right tire flat Left tire flat (Roumeliotis, Sukhatme, Bekey 98a)

12. 12/53 Gyro Failure Residuals post-processed with Bayesian hypothesis testing (Roumeliotis, Sukhatme and Bekey 98b)

13. 13/53 Multi-node Mapping

14. 14/53 Algorithm Outline Individual nodes detect features Individual nodes create topological maps with approximate metric information Match algorithm finds best match using heuristic pruning Combined map displayed to user

15. 15/53 Landmark Detection and Mapping

16. 16/53 Map Representation Augmented graph –Vertices are landmark elements –Links are metric connections struct node{ id type// corner, junction, door x, y// approx coordinates struct link[4]// 4 possible // directions visit_counter detection_counter } struct link{ connected_to_id type// open space, blocked, // door heading// in local frame compass distance travel_counter }

17. 17/53 Area Explored Robot 1 Robot 2 Both

18. 18/53 Individual Maps

19. 19/53 Candidate Transformations Two maps (graphs) with n and m nodes respectively General graph isomorphism problem is NP complete Most known algorithms are procedural in nature and exponential in complexity

20. 20/53 Match Heuristics H1: pair landmarks of same type attribute (e.g doors with doors, corners with corners) H2: consider only landmarks that describe invariant spatial features (e.g. corners and junctions)

21. 21/53 Scaling Reduces the number of candidate transformations to about 20% of n*m

22. 22/53 Final Matching Check for mismatch in absolute heading value Compute translation and rotation transformation for each remaining pair The transformation that yields the highest number of overlapping landmarks is the best match

23. 23/53 Results of Match Algorithm

24. 24/53 Results Tested on over 50 trials over ~2 km of traverse –heuristic pruning never resulted in best match being discarded –in 96% of the cases, top two matches produced contain the correct result (Dedeoglu and Sukhatme 00)

25. 25/53 Pointers Papers robotics.usc.edu/~gaurav/publications.html Projects SCOWRnetweb.usc.edu/scowr MARSrobotics.usc.edu/projects/mars TMRrobotics.usc.edu/projects/tmr SUOrobotics.usc.edu/~avatar