Robust Weighted Scan Matching with Quadtrees

Robust Weighted Scan Matching
with Quadtrees
Arnoud Visser, Bayu Slamet and Max Pfingsthorn
5th International Workshop on Synthetic Simulation and Robotics
to Mitigate Earthquake Disaster (SRMED 2009), Graz, July 2009
Universiteit van Amsterdam
Informatica Instituut

Virtual Rescue League
Simultaneously:
• Where am I (Localization)
• Where have I been (Mapping)
• Find the victims (Exploration)
• Share this information and
coordinate with others
(Distribituted Decision making)

A wide variety of worlds

A wide variety of Robotic platforms

Networked robot team

UsarCommander
• User Interface can be used to teleoperate the
robots and monitor the shared map.
• In our approach, both the robots and the
basestation maintain a distributed map
(each with a partial view of the word).

Pose estimation
Pose & confidence
• Several Scan Matching algorithms

Scan matching
• This is a search process, where the
position is estimated by shifting a robot
around, until a measure is minimized

Several algorithms evaluated
• Iterative Dual Correspondence
– Estimate translation from Euclidean distance
– Estimate rotation from Polar distance
• Metric-based Iterative Closest Point
– Estimate translation and rotation from
a combined distance measure
• Weighted Scan Matching
– Estimate translation and rotation from Euclidean
distance only, but weight the contribution of each pair
on the correspondence and measurement uncertainty

Correspondence error
Odometry estimate Correspondence error After match

Localization vs Mapping
• When the confidence is good, the current
position fits to one of the positions of the
local submap
(localization, no extension of the map)
• When confidence drops, a new patch is
added to the chain
(graph based mapping)

Chain of patches
π = laser scan
θ = absolute location (Euclidean)
Φ= relation
= relative location (polar) Δθ + covariance matrix Σ

Localization vs Mapping
• Graph based map allows a distributed
approach, which scales well
for multiple robots
• High accuracy maps can be achieved
• Additional data can be added
asynchronously
• Results have been validated
See Max Pfingsthorn, Bayu Slamet and Arnoud Visser, "A Scalable Hybrid Multi-Robot SLAM Method
for Highly Detailed Maps", Lecture Notes on Artificial Intelligence series volume 5001, p. 457-464,
Springer, Berlin Heidelberg New York, July 2008.

Localization vs Mapping
• Graph based map allows a distributed
approach, which scales well
for multiple robots
• High accuracy maps can be achieved
• Additional data can be added
asynchronously
• Results have been validated
See Max Pfingsthorn, Bayu Slamet and Arnoud Visser, "A Scalable Hybrid Multi-Robot SLAM Method
for Highly Detailed Maps", Lecture Notes on Artificial Intelligence series volume 5001, p. 457-464,
Springer, Berlin Heidelberg New York, July 2008.

Remaining Mapping error
IDC
WSM

Not enough reference points
Scan
time 1 time 2 time 3

Short or long term memory helps
Scan
time 3 missing section available from time 1

esidual correlation distance
mm
IDC
WSM
Local map (blue) versus global map (red)

Matching covariance
mm
IDC
WSM
Local map (black) versus global map (blue)

Remaining Mapping error
Q-IDC
WSM

Q-WSM benefits more
Global map available in quadtree
allows to tighten thresholds

Validation on Radish datasets

Comparison with full SLAM
• All results are based
on iterative SLAM
• Results can further
improve
with post-processing
the data

Inter League Challenge 2009
Partial map of the Graz 2009 Arena

Conclusion
• The correspondence error can be reduced by
providing a denser matching set
• This provides increased robustness and
accuracy, especially for WSM.
• With a quadtree implementation this can be done
in realtime
2 nd place
BRAZIL OPEN
3rd place
4 th place 1 st place
3rd place