Avneesh Sud Vaibhav Vaish under the guidance of Dr. Subhashis Banerjee

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Presentation transcript:

Avneesh Sud Vaibhav Vaish under the guidance of Dr. Subhashis Banerjee Robot Navigation Avneesh Sud Vaibhav Vaish under the guidance of Dr. Subhashis Banerjee

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

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

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

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

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

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)

Line Correspondence -Results

Line Correspondence -Results

Mid-point of shortest distance Reconstruction Mid-point of shortest distance

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 TO BE DONE Identification of ground plane. Current implementation projects onto xz plane of camera co-ordinate system.

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 OUR TARGET To complete the above by the end of the semester

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.

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.