SLAM Using Single Laser Range Finder

1
SLAM Using Single Laser Range Finder

• AliAkbar Aghamohammadi, Amir H. Tamjidi, Hamid D.
  Taghirad
• Advance Robotic and Automation Systems Lab
  (ARAS),Electrical and Computer Engineering
  DepartmentK. N. Toosi University of Technology,
  Iran

2
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Error Modeling For Individual Features
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

3
Motivation

• traditional encoder-base dynamic modeling are
  sensitive to
• slippage
• surface type changing
• imprecision in the parameters of robot's
  hardware.

LSLAM is a significant step toward encoder-free
SLAM and it is robust with respect to slippage
and problems associated with encoder-base motion
models.
4
Main Contributions

• The key contributions of LSLAM include

1. Robust feature extraction method
2. Accurate error modeling for individual extracted
   features
3. Uncertainty estimation in feature-based range
   scan matching
4. Achieving real-time drift-free solution for SLAM
   in restricted structured environments using a
   single laser range finder as the only data source

5
Probabilistic Framework

• State Vector of the system comprises of robot
  pose and spatial features, represented in world
  coordinates
• 
  
• 
  
• At system start-up, feature-based map is
  initialized this map is updated dynamically by
  the Extended Kalman Filter until operation ends.
  The probabilistic state estimates of the robot
  and features are updated during robot motion and
  feature observation. When new features are
  observed the map is enlarged with new states.

6
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Reliability Measure Calculation
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

7
Feature Extraction
Point Features
Line Features
More Informative Features

• invariant wrt displacement
• robust wrt data association

Features have to be
8
Feature Extraction
Steps
features
9
Omitting variant features

• There exist two kind of variant features
• Those, appear due to occlusion
• Those, appear due to low incidence angle

10
Feature Extraction Results
Extracted Features
11
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Reliability Measure Calculation
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

12
Reliability Measure CalculationFor Individual
Features

• Feature uncertainty
• Observation noise
• Uncertainty due to quantization

13
Measurement noise
pi
er
ri
e?
14
Quantization Error
This issue causes that the point pi, considered
as a feature point, not necessarily be the same
physical feature in the environment.
fk (real feature in the environment)
Pi (selected edge feature)
ri1
ri
ri-1
ß
ß
15
Feature Covariance

• Measurement and quantization errors are
  independent from each other

16
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17
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Reliability Measure Calculation
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

18
Motion Prediction

• Traditional models, based on encoders' data,
  suffer from some problems in motion modeling such
  as wheel slippage, unequal wheel diameters,
  unequal encoder scale factors, inaccuracy about
  the effective size of wheel base, surface
  irregularities, and other predominantly
  environmental effects

19
Motion Prediction

• we use a prediction model, which does not merely
  rely on robot, but it uses environmental
  information too. Thus, method is robust with
  respect to wheel slippage, surface changing and
  other unsystematic effects and inaccurate
  information about robot's hardware.

20
Motion Prediction

• Matching
• Pose Shift Calculation
• ( Cost function based on weighted
  feature-based Range scan matching )

21
Motion Prediction Uncertainty Calculation

• If there was an explicit relationship between
  features and pose shift
• Indeed, Since T and R have to minimize the cost
  function E, we have an implicit relationship
  derived from
• X contains the parameters
• of T and R.
• Thus there is an implicit relationship between
  features and pose shift.

But there is not !!!

22
Motion Prediction Uncertainty Calculation

• The implicit function theory can provide the
  desired Jacobian via below equation

23
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Reliability Measure Calculation
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

24
Data association

• Batch data association methods greatly reduce the
  ambiguity in data association process. Thus, here
  JCBB method is adopted for data association.
• After data association process, extracted
  features from new scan fall into two categories
• New features, which are not matched with any
  existent feature in the map
• Existing features, (matched ones)

25
Filtering and Adding New features

• Existing features, (matched ones) are used to
  update the system state vector
• Each newly seen feature is first transformed to
  the map reference coordinate and then the
  transformed feature is augmented with the system
  state vector.

26
Outline

• 1-Motivation Contributions
• 2-Probabilistic Framework
• 3-Feature Extraction
• 4-Reliability Measure Calculation
• 5-Motion Prediction
• 6-Data Association
• 7-Adding new features
• 8-Filtering (IEKF)
• 9-Results
• 10-Conclusion
• 11-Refrences

27
Results

• Melon a tracked mobile robot
• equipped with two low range
• Hokuyo URG_X002
• laser range scanners
• (High Slippage)

An Structured Environment
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Pure Localization
ICP Method
ICP method is a popular point-wise method. It is
a powerful method, but it needs prior information
about displacement.
29
Results(Pure Localization)
HAYAI Method
HAYAI method produces impressive results in term
of processing speed. But it suffers from some
disadvantages.
30
Pure Localization
Proposed motion model
31
LSLAM
Simulation Results

• The environment consists
• of many features.
• Ground truth is available
• Loop closing effects can be
• investigated in a large loop

32
LSLAM - Simulation
Error in x
Error in ?
Estimated errors (blue curves) and estimated
variances (red curves) in x, y and theta (robot
heading)
Error in y
33
LSLAM (real scan data)
LSLAM
Feature-based map resulted from LSLAM
Pure Localization
34
8-Conclusion

• introducing robust motion model with respect to
  robot slippage and inaccuracy in hardware-related
  measures
• calculating reliability measure for robots
  displacement derived through the feature-based
  laser scan matching
• Extract features in different scales
• construct an IEKF framework merely based on laser
  range finder information

35
9-References

• 1 Robot pose estimation in unknown environments
  by matching 2D range scans. Lu, F. and Milios, E.
  1997. 1997, Journal of Intelligent and Robotic
  Systems, Vol. 18, pp. 249-275.
• 2 Metric-based scan matching algorithms for
  mobile robot displacement estimation. Minguez,
  J., Lamiraux, F. and Montesano, L. 2005.
  Barcelona, Spain.  s.n., 2005. Proceedings of
  the IEEE International Conference on Robotics and
  Automation (ICRA).
• 3 Scan alignment with probabilistic distance
  metric. AJensen, B. and Siegwart, R. 2004. 2004.
  Proceedings of the IEEE/RSJ International
  Conference on Intelligent Robots and Systems.
• 4 Weighted range sensor matching algorithms for
  mobile robot displacement estimation. Pfister,
  S., et al. 2002. s.l.  Proceedings of the IEEE
  International Conference on Robotics and
  Automation (ICRA02), 2002. pp. 1667-1674.
• 5 Feature-Based Laser Scan Matching For
  Accurate and High Speed Mobile Robot
  Localization. Aghamohammadi, A.A., et al. 2007.
  s.l.  European Conference on Mobile Robots
  (ECMR07), 2007.
• 6 High-speed laser localization for mobile
  robots. Lingemann, K., et al. 2005. 4, s.l. 
  Journal of Robotics and Autonomous Systems, 2005,
  Vol. 51, pp. 275296.
• 7 Natural landmark-based autonomous vehicle
  navigation. Madhavan, R. and Durrant-Whyte, H. F.
  2004. s.l.  Robotics and Autonomous Systems,
  2004, Vol. 46, pp. 79-95.
• 8 Mobile robot positioning with natural
  landmark. Santiso, E., et al. 2003. Coimbra,
  Portugal  s.n., 2003. Proceedings of the 11th
  IEEE International Conference on Advanced
  Robotics (ICAR03). pp. 47-52.
• 9 Recursive Scan-Matching SLAM. Nieto, J.,
  Bailey, T. and Nebot, E. 2007. 1, s.l.  Journal
  of Robotics and Autonomous Systems, January 2007,
  Vol. 55, pp. 39-49.
• 10 Nieto, J. 2005. Detailed environment
  representation for the slam problem. Ph.D.
  Thesis. s.l.  University of Sydney, Australian
  Centre for Field Robotics, 2005.
• 11 Globally consistent range scan alignment for
  environment mapping. Lu, F. and Milios, E. 1997.
  Autonomous Robots, 1997, Vol. 4, pp. 333349.
• 12 An Interior, Trust Region Approach for
  Nonlinear. Coleman, T.F. and Y., Li. 1996. s.l. 
  SIAM Journal on Optimization, 1996, Vol. 6, pp.
  418-445.
• 13 Data association in stochastic mapping using
  the joint compatibility test. Neira, J. and
  Tardos, J.D. 2001. 6, s.l.  IEEE Transactions on
  Robotics and Automation, 2001, Vol. 17, pp.
  890897.
• 14 Gelb, A. 1984. Applied Optimal Estimation.
  s.l.  M.I.T. Press, 1984.
• 15 A real-time algorithm for mobile robot
  mapping with applications to multi-robot and 3D
  mapping. Thrun, S., Bugard, W. and Fox, D. 2000.
  s.l.  International Conference on Robotics and
  Automation, 2000. pp. 321328.