Autonomous Localization & Navigation using 2D Laser

Scanners

Autonomous Localization & Navigation using 2D Laser

Scanners

Animesh Garg & Manohar Paluri

22

OutlineOutline

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

33

Why an autonomous painting system is required?Why an autonomous painting system is required?

44

IntroductionIntroduction

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.

55

Potential SolutionsPotential Solutions

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

66

Our solutionOur solution

Sample Mount

• Laser Scanners– Dense, Accurate, High

sampling rates, good range distance.

– Paint Hangar constraints

77

Past WorkPast 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.

88

Block DiagramBlock Diagram

99

Rigid TransformationRigid Transformation

• Scan1 Scan2

• Combined Scan

1010

Line Extraction TechniquesLine 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 ?

1111

Split & MergeSplit & 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.

1212

Hough TransformHough 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

1313

Non-Uniform DensityNon-Uniform Density

1414

Weighted Hough TransformWeighted Hough Transform

1515

Find Maximas – 5 constraintsFind 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.

1616

Line FittingLine Fitting

1717

Line Fitting - ExampleLine Fitting - Example

1818

LocalizationLocalization

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

1919

Obstacle MapObstacle 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

2020

Obstacle DetectionObstacle Detection

2121

Planning – RRT* 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.

2222

NavigationNavigation

• 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.

2323

Testing on SegwayTesting on Segway

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

2424

Conclusion & Future ScopeConclusion & Future Scope

2525

RecapRecap

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