Autonomous Localization & Navigation using 2D Laser

Scanners

Animesh Garg & Manohar Paluri

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Outline

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

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Why an autonomous painting system is required?

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Introduction

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.

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.

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Potential Solutions

• Cameras• GPS• INS• Sonar• Laser• Northstar• Vicon• And more…

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Our solution

Sample Mount

• Laser Scanners– Cost effective, Reliable,

Accurate, known solutions!

– Paint Hangar constraints

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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.

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Block Diagram

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Rigid Transformation

• Scan1 Scan2

• Combined Scan

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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 ?

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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.

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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

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Non-Uniform Density

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Weighted Hough Transform

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Find Maximas – 5 constraints

1. They appear in pairs: the first one is formed by peaks H1 and H2; the second one is formed by peaks H3 and H4.

2. Two peaks belonging to the same pair are symmetric with respect to the x-axis(angle).3. The two pairs are separated by 90o4. The heights of the two peaks within the same pair are exactly the same, and represent

the length of the respective line segment.5. 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.

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Line Fitting

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Line Fitting - Example

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Localization

• Distances to four walls• Accurate with a radius

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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

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Obstacle Detection

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Planning - RRT

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Navigation

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Future Scope

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Thank You