1 / 92

Landmark Selection for Vision-Based Navigation

Landmark Selection for Vision-Based Navigation. Pablo L. Sala Joint work with Robert Sim, Ali Shokoufandeh and Sven Dickinson To be presented in IROS 2004 September 17 th , 2004. Robot Navigation. [Leonard and Durrant-Whyte] Where am I? Where am I going? How do I get there? .

lindsay
Télécharger la présentation

Landmark Selection for Vision-Based Navigation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Landmark Selection for Vision-Based Navigation Pablo L. Sala Joint work with Robert Sim, Ali Shokoufandeh and Sven Dickinson To be presented in IROS 2004 September 17th, 2004

  2. Robot Navigation • [Leonard and Durrant-Whyte] • Where am I? • Where am I going? • How do I get there?

  3. Robot Navigation • [Leonard and Durrant-Whyte] • Where am I? Localization • Where am I going? • How do I get there?

  4. Robot Navigation • [Leonard and Durrant-Whyte] • Where am I? Localization • Where am I going? Goal Identification • How do I get there?

  5. Robot Navigation • [Leonard and Durrant-Whyte] • Where am I? Localization • Where am I going? Goal Identification • How do I get there? Path-planning

  6. Robot Navigation • [Leonard and Durrant-Whyte] • Where am I? Localization • Where am I going? Goal Identification • How do I get there? Path-planning

  7. Landmark-Based Navigation • What makes a good landmark?

  8. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?)

  9. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility

  10. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks?

  11. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks? • Manually

  12. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks? • Manually • Automatically

  13. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks? • Manually • Automatically… but how?

  14. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks? • Manually • Automatically • Store every landmark visible at each location (costly!)

  15. Landmark-Based Navigation • What makes a good landmark? • Distinctiveness (does it tell me where I am?) • Wide Visibility • How do we select good landmarks? • Manually • Automatically • Store every landmark visible at each location (costly!) • Find smallest subset of landmarks that supports reliable navigation (optimal!)

  16. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization

  17. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Collection of images acquired at known discrete points in pose space. Pose recorded and image features extracted and stored in database.

  18. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Collection of images acquired at known discrete points in pose space. Pose recorded and image features extracted and stored in database.

  19. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Collection of images acquired at known discrete points in pose space. Pose recorded and image features extracted and stored in database.

  20. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Collection of images acquired at known discrete points in pose space. Pose recorded and image features extracted and stored in database.

  21. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Collection of images acquired at known discrete points in pose space. Pose recorded and image features extracted and stored in database.

  22. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization

  23. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization Four features are needed in this set.

  24. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization Four features are needed in this set. Only two features needed. Our goal is to find this decomposition.

  25. View-Based Robot Navigation Off-line Exploration Landmark Database Construction On-line Localization • Current pose is estimated using the locations of a small number of features in the current image, matched against their locations in two model views.

  26. View-Based Robot Navigation

  27. View-Based Robot Navigation

  28. View-Based Robot Navigation

  29. View-Based Robot Navigation

  30. View-Based Robot Navigation

  31. View-Based Robot Navigation

  32. View-Based Robot Navigation

  33. View-Based Robot Navigation

  34. View-Based Robot Navigation

  35. View-Based Robot Navigation

  36. View-Based Robot Navigation

  37. Intuitive Problem Formulation

  38. Outline • Problem Formulation • Complexity • Heuristic Methods • Results on Synthetic and Real Images • Conclusions

  39. A Graph Theoretic Formulation Problem Definition: The -Minimum Overlapping Region Decomposition Problem (-MOVRDP) for a world instance <G=(V,E), F, {v}vV> consists of finding a minimum size -overlapping decomposition D = {R1, …, Rd} of V into regions such that:

  40. A Graph Theoretic Formulation Problem Definition: The -Minimum Overlapping Region Decomposition Problem (-MOVRDP) for a world instance <G=(V,E), F, {v}vV> consists of finding a minimum size -overlapping decomposition D = {R1, …, Rd} of V into regions such that: Theorem 1: A -MOVRDP can be reduced to an equivalent 0-MOVRDP, and the solution to this latter problem can be extended to a solution for the original problem.

  41. A Graph Theoretic Formulation Problem Definition: The -Minimum Overlapping Region Decomposition Problem (-MOVRDP) for a world instance <G=(V,E), F, {v}vV> consists of finding a minimum size -overlapping decomposition D = {R1, …, Rd} of V into regions such that: Theorem 1: A -MOVRDP can be reduced to an equivalent 0-MOVRDP, and the solution to this latter problem can be extended to a solution for the original problem. Theorem 2: The decision problem <0-MOVRDP, d> is NP-complete. (Proof by reduction from the Minimum Set Cover Problem.)

  42. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms.

  43. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region:

  44. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 25

  45. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 25

  46. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 19

  47. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 19

  48. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 19

  49. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 19

  50. Heuristic Methods for 0-MOVRDP • 0-MOVRDP is intractable. • Optimal decomposition not needed in practice. • We developed and tested six greedy approximation algorithms. • Algorithm A.x: O(|V|2|F|) k = 4 Features commonly visible in region: 17

More Related