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Autonomous Localization & Navigation using 2D Laser Scanners

Autonomous Localization & Navigation using 2D Laser Scanners. Animesh Garg & Manohar Paluri. Outline. Problem Description Motivation Previous research Proposed approach Details of our approach Testing Results Conclusion. Why an autonomous painting system is required?. Introduction.

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Autonomous Localization & Navigation using 2D Laser Scanners

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  1. Autonomous Localization & Navigation using 2D Laser Scanners Animesh Garg & ManoharPaluri

  2. Outline • Problem Description • Motivation • Previous research • Proposed approach • Details of our approach • Testing • Results • Conclusion

  3. Why an autonomous painting system is required?

  4. Introduction The Omnimove is a huge platform for moving very heavy weights around. Herein it would hold the robotic arm which will be used to carry out the painting job. During spray painting process, the environment has a very large concentration of paint particles decreasing visibility. And the paint settles on surfaces, it rules out markers based solution.

  5. Potential Solutions • Cameras • GPS • INS • Sonar • Laser • Northstar • Vicon • Infrared • And more…

  6. Our solution • Laser Scanners • Dense, Accurate, High sampling rates, good range distance. • Paint Hangar constraints Sample Mount

  7. Past Work • Fast RANSAC based registration algorithm for accurate navigation using only Lidar. RANSAC in combination with Huber's kernel to overcome the LIDAR input noise. • Hough transform for robot localization.The self localiza-tion technique in the paper is based on matching a geometric reference map with range information • RRT-Connect, bi-directional decision trees. • RRT* - Combines advantages of RRGs optimal solution with a tree structure.

  8. Block Diagram

  9. Rigid Transformation • Scan1 Scan2 • Combined Scan

  10. Line Extraction Techniques • Split-and-Merge • Line-Regression • Incremental • RANSAC • Hough-Transform • EM How many lines are there ? Which points belong to which line ? Given the points that belong to a line, how to estimate the line model parameters ?

  11. Split & Merge • Initial: set s1 consists of N points. Put s1 in a list L 1 • Fit a line to the next set s in L 2 • Detect point P with maximum distance d to the line 3 • If d is less than a threshold, continue (go to 2) 4 • Otherwise, split s at P into s 1 and s 2, replace s in 5 • L by s 1 and s 2, continue (go to 2) • When all sets (segments) in L have been checked, 6 • merge collinear segments.

  12. Hough Transform • Initial: A set of N points • Initialize the accumulator array (model space) • Construct values for the array • Choose the element with max. votes Vmax • If Vmax is less than a threshold, terminate • Otherwise, determine the inliers • Fit a line through the inliers and store the line • Remove the inliers from the set, goto 2

  13. Non-Uniform Density

  14. Weighted Hough Transform

  15. Find Maximas – 5 constraints • They appear in pairs: the first one is formed by peaks H1 and H2; the second one is formed by peaks H3 and H4. • Two peaks belonging to the same pair are symmetric with respect to the x-axis(angle). • The two pairs are separated by 90o • The heights of the two peaks within the same pair are exactly the same, and represent the length of the respective line segment. • The vertical distances between peaks within the pair are exactly the sides of the rectangle. In case of other obstacles in the scene, constraints 4 & 5 are not robust. So we only use 1, 2 & 3.

  16. Line Fitting

  17. Line Fitting - Example

  18. Localization • Sum of distance to four walls is a constant. • Accurate with a radius

  19. Obstacle Map We are provided with a map of the surrounding which mark the non-navigable areas. As an emulation we use a central area on the map and render it as non-navigable while testing the localization module on Segway

  20. Obstacle Detection

  21. Planning – RRT* • Incremental Sampling based Planning Algorithm • Asymptotic Optimality Guarantee • Similar to RRT and/or RRG (Rapidly exploring Random Graphs) in the sense that they all have same body. However RRT* calculates the minimum cost from initial to current local goal point. Hence at every move from current configuration to a new configuration, accumulated cost of moving from start to present state.

  22. Navigation • Use of Omnimove affects the conventional motion planning implementations. • Ability to manipulated in all the directions make it easy to go to all the possible configurations.

  23. Testing on Segway • Implementation of the Navigation module on the Segway platform for testing in partially known

  24. Conclusion & Future Scope

  25. Recap • Boeing Paint Hangar automation • Hough Transform & Split-Merge line extraction • Hough rectangle constraints • Obstacle avoidance • RRT* planning • Segway testing • Final testing

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