Mobile Robot Localization and Mapping Using Range Sensor Data

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Mobile Robot Localization and Mapping Using Range
Sensor Data
Dr. Joel Burdick, Dr. Stergios Roumeliotis,
Samuel Pfister, Kristo Kriechbaum
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Motivation

• Goal Improved Localization
• The absolute position error of a robot
  accumulates due to
• - Wheel slippage
• - Sensor drift
• - Sensor error
• Without correction the error growth is unbounded
• Accurate knowledge of absolute position is
  necessary for effective navigation and accurate
  mapping

• Goal Efficient Map Representation
• By abstracting raw sensor data into
  representative features we
• - require less storage space
• - reduce the computation complexity for many
  mapping and localization algorithms
• The data representation must not filter out
  excessive useful data

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Testbed Equipment
Mobile Robot - On board Pentium III PC
SICK Laser Range Scanner - 8 meter range / 1 mm
resolution - 180 degree field-of-view
Laser Scanner
Robot
Range Measurements
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Goal Improved Localization
Scan Matching Align two range scans taken at
different poses to calculate an improved
displacement estimate.
Method - Discard outliers - Correspond closest
points across scans - Use iterative Maximum
Likelihood algorithm to calculate an optimal
displacement estimate
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Weighted Approach
Explicit models of uncertainty noise sources
are developed for each scan point taking into
account - Sensor noise errors - Point
correspondence uncertainty
Improvements vs. Unweighted Method
- More accurate displacement estimate - More
realistic covariance estimate - Increased
robustness to initial conditions - Improved
convergence properties
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Weighted Formulation
Goal Estimate displacement (pij ,?ij )
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Maximum Likelihood Estimation

• Position displacement estimate obtained in
  closed form

• Orientation estimate found using 1-D numerical
  optimization,
• or series expansion approximation methods

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Localization Results
Weighted vs. Unweighted matching of two poses
512 trials with different initial displacements
within /- 15 degrees of actual angular
displacement /- 150 mm of actual spatial
displacement
- Increased robustness to inaccurate initial
displacement guesses - Fewer iterations for
convergence
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Localization Results

• Displacement estimate errors at end of path
• Odometry 950mm
• Unweighted 490mm
• Weighted 120mm

- More accurate covariance estimate - Improved
knowledge of measurement uncertainty - Better
fusion with other sensors
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Goal Efficient Mapping
Line Fitting - Given a group of range
measurements, each with a unique uncertainty,
determine the set of optimally fit lines as well
as the uncertainty of those lines.
Method - Group roughly collinear points using
Hough Transform - Calculate optimal line fit
using Maximum Likelihood framework with each
point weighted according to its individual
uncertainty - Calculate uncertainty of line fit -
Merge similar lines across data scans for further
map simplification
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Line Fitting Results
Fig. A Fig. B Fig. C
Map built from laser range scans taken from 10
poses in a hallway
Fig. A Raw Points with associated
uncertainties - 7200 raw range points Fig. B
Fit lines with associated uncertainties - 114
fit lines Fig. C Final line map after line
merging - 46 fit lines
Final data compression 98.7
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Line Fitting Results
Raw Range Data Points Taken in Lab (10 Poses,
7200 Points)
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Line Fitting Results
141 Lines Fit
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Line Fitting Results
74 Lines After Merging (97.9 Compression)
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Future Work
- Transition to CCD camera as primary sensor -
Extend theory to include non-planar features -
Extract features invariant to small changes in
robot displacement - Identify a metric to
measure which features maximally distinguish
between locations - Establish a general
framework for automatic feature selection -
Merge multiple sensors for optimal localization
and mapping