1. CSE-473 Mobile Robot Mapping

2. Mapping with Raw Odometry

3. Why is SLAM a hard problem? SLAM: robot path and map are both unknown Robot path error correlates errors in the map

4. Why is SLAM a hard problem? In the real world, the mapping between observations and landmarks is unknown Picking wrong data associations can have catastrophic consequences Pose error correlates data associations Robot pose uncertainty

5. Graphical Model of Mapping m x z u x z u 2 2 x z u... t t x 1 1 0 10t-1

6. Map with N landmarks:(3+2N)-dimensional Gaussian Can handle hundreds of dimensions (E)KF-SLAM

7. EKF-SLAM Map Correlation matrix

8. EKF-SLAM Map Correlation matrix

9. EKF-SLAM Map Correlation matrix

10. Victoria Park Data Set [courtesy of E. Nebot]

11. Victoria Park Data Set Vehicle [courtesy of E. Nebot]

12. Data Acquisition [courtesy of E. Nebot]

13. Estimated Trajectory [courtesy of E. Nebot]

14. Graph-SLAM Idea

15. Mapping the Allen Center

16. Comparison to “Ground Truth Map”

17. Three Mapping Runs

18. Three Overlayed Maps

19. 3D Outdoor Mapping 10 8 features, 10 5 poses, only few secs using cg.

20. Map Before Optimization

21. Map After Optimization

22. Autonomous Navigation Courtesy of W. Burgard

23. Rao-Blackwellized Mapping Compute a posterior over the map and possible trajectories of the robot : robot motionmap trajectory map and trajectory measurements

24. FastSLAM Robot Pose2 x 2 Kalman Filters Landmark 1Landmark 2Landmark N … x, y,  Landmark 1Landmark 2Landmark N … x, y,  Particle #1 Landmark 1Landmark 2Landmark N … x, y,  Particle #2 Landmark 1Landmark 2Landmark N … x, y,  Particle #3 Particle M … [Begin courtesy of Mike Montemerlo]

25. Example map of particle 1map of particle 3 map of particle 2 3 particles

26. Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle

27. Frontier Based Exploration [Yamauchi et al. 96], [Thrun 98]