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Graph-SLAM for Mobile Robot Mapping Optimization

Explore Graph-SLAM, loop closure, and Rao-Blackwellized Mapping for efficient robot mapping. Learn about FastSLAM, scan-matching, and landmark tracking for accurate outdoor mapping. Discover multi-robot mapping and frontier-based exploration techniques.

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Graph-SLAM for Mobile Robot Mapping Optimization

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  1. CSE-473 Mobile Robot Mapping

  2. Mapping with Raw Odometry

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

  4. Graph-SLAM Idea

  5. Robot Poses and Scans [Lu and Milios 1997] • Successive robot poses connected by odometry • Sensor readings yield constraints between poses • Constraints represented by Gaussians • Globally optimal estimate

  6. Loop Closure • Use scan patches to detect loop closure • Add new position constraints • Deform the network based on covariances of matches Before loop closure After loop closure

  7. Mapping the Allen Center

  8. 3D Outdoor Mapping 108 features, 105 poses, only few secs using cg.

  9. Map Before Optimization

  10. Map After Optimization

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

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

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

  14. Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle Loop Closure

  15. Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle Loop Closure

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

  17. Example (Intel Lab) • 15 particles • four times faster than real-timeP4, 2.8GHz • 5cm resolution during scan matching • 1cm resolution in final map joint work with Giorgio Grisetti

  18. FastSLAM – Simulation • Up to 100,000 landmarks • 100 particles • 103 times fewer parameters than EKF SLAM Blue line = true robot path Red line = estimated robot path Black dashed line = odometry

  19. Victoria Park Results • 4 km traverse • 100 particles • Uses negative evidence to remove spurious landmarks Blue path = odometry Red path = estimated path [End courtesy of Mike Montemerlo]

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

  21. Multi-Robot Mapping Robot A Robot B Robot C

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