MELT10: Real-Time Indoor Mapping for Mobile Robots with Limited Sensing Ying Zhang, Juan Liu and Gabriel Hoffmann Palo Alto Research Center Palo Alto, CA, 94304 Mark Quilling, Kenneth Payne, Prasanta Bose and Andrew Zimdars Lockheed Martin Space Systems Center Sunnyvale, CA, 94089

© 2010 PARC

Overview

• Motivation • Formalization • Solution • Experimentation • Conclusion

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Landroids DARPA 2007 - 2010

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Motivation

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•  Target Applications: •  Robotic Relay and Sensing Networks, with •  Small and cheap robotic platforms

•  Requirements: •  Mapping and localization with short-range

sensors •  Real-time and efficient solutions

Platform

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Bumpers Wall IR Sensor

Approach

• Wall Following –  Never being trapped –  Coverage guaranteed

• Outline Mapping –  Efficient representation for small platforms –  Effective use of environment constraints to

compensate sensing noise –  Efficient loop detection and map merging

• Algorithms –  Segmentation –  Rectification

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Outline Map

Segmentation •  Input: trajectory points (x, y, bl, wr) •  Output: line segments (length, θ, turn)

•  Why trace segmentation? –  Saves computation, work on line segments which are much fewer

than individual trajectory points –  Even out noise for wall-following robots.

•  Algorithm: line-growing algorithm Grow a line as much as you can unless –  Bumper/wall sensor suggests a turn, or –  A sharp angle with respect to the current orientation is identified.

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Challenge

Noisy Odometry

Floor Plan

Hallway

Rectification

•  Assumption: Most indoor corners are rectilinear •  Conclusion: Only need to classify any turn angle into

(0, 90, 180, 270) degrees. •  Solution: Simple but cannot handle pathological case! •  Probabilistic state-based (with past history) estimation

–  Get over the pathological case –  Works around obstacles –  Can accommodate non-rectilinear angles

Formalization

Two models needed: (why?) •  Dynamics model

•  Observation likelihood model: –  Angle noise model:

n is noise, modeled as Generalized Gaussian Distributed (GGD) why --- heavy tail to handle large noise

–  Turning type model:

•  History based state: s =

⎩⎨⎧ −

=consistentconsistentnon

sZp turn

15.0

)|(

State Obs: (θ, turn type)

Real-time Mapping •  Each hypothesis is a line segment map •  Given a new segment, each hypothesis forks into four possibilities:

straight, left, right, U-turn •  Compute each new hypothesis and keep up to N (100) best

hypotheses •  Return the most probable hypothesis as the final mapping result.

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prob= 0.15

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prob= 0.12

Outline Map Features

Decreasing θ

Feature Correlation from Two Maps Feature Match

Loop Detection

Map Merging

Experimentation

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Demonstration

Future Work

• Loop Detection • Loop Closing • Map Merging • Map Annotation • Map-Guided Navigation • Map-Guided Deployment

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Conclusion

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• Simple yet Powerful Indoor Map Representation

• Efficient Computation based-on Probabilistic Modeling and Reasoning

• Reliable System Integration with Real Demonstrations

Thanks to: DARPA LANdroids Project