mid_term1 (2).pptRobotic VisionRobotic Vision

1. Robot Navigation 2 Robot Navigation 2

2. Problem Description Problem Description The aim is to enable a robot to move around on the ground plane by visually detecting and avoiding obstacles.

3. Stages Involved (Existing) Stages Involved (Existing) Camera Calibration Edge & Corner Detection Finding Correspondences 3-D Reconstruction Path Planning Navigating the Path

4. Camera Calibration Camera Calibration m = Ai [Ri|ti] M  A co-ordinate system for each camera  4+3+3 = 10 unknowns

9. Right Image plane Left Image plane Essential & Fundamental Matrices Essential & Fundamental Matrices e’ e x x’ R,T Y’ X’ Z’ C’ Right optical center Base Line X Y Z C Left optical center X World Point • E = [T]XR • X’T EX = 0 • F = C’-T EC-1 • x’T Fx = 0 • l’ = Fx l’

11. Corner Detection Corner Detection Toolkit used : horatio  Edges are determined  Line Map is fit on the edges  Junctions of the lines are found

13. Corner Detection Corner Detection Toolkit used : horatio  Edges are determined  Line Map is fit on the edges  Junctions of the lines are found

14. Finding Correspondences (Lines) Finding Correspondences (Lines) Candidate Lines should have similar orientation Images of end-points are got using the epipolar constraint An new approach considers orientation of nearby lines (Amit Garg)

15. Line Correspondence -Results Line Correspondence -Results

16. Line Correspondence -Results Line Correspondence -Results

17. Reconstruction Reconstruction Mid-point of shortest distance

18. Locating Obstructions Locating Obstructions The reconstructed scene is projected on the ground plane. Clustering is done, by deleting long edges in MST Each cluster is bounded by its convex hull Identification of ground plane. Current implementation projects onto xz plane of camera co-ordinate system. TO BE DONE

21. Needed for Navigation Needed for Navigation Hand-eye calibration: to locate robot w.r.t. co-ordinate system Visual Servoing: using visual feedback for correction in motion Path Planning: Simple backtracking algorithm To complete the above by the end of the semester OUR TARGET

22. New Approaches New Approaches Avoid Calibration by: Self-Calibration this was attempted by Amit Garg & Deepak Verma, without much success. Inner Camera Invariants this will let us handle varying or unknown internal parameters of the camera.

23. References References  Three Dimensional Computer Vision O. Faugeras  The Geometry of Multiple Views Andrew Zissermann  A Versatile Camera Calibration Technique Roger Tsai (IEEE J. of Rob. & Aut., 1987)  Inner Camera Invariants & Applications S. Banerjee et al.

mid_term1 (2).pptRobotic VisionRobotic Vision

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