SLAM Survey: Simultaneous Localization and Mapping Martin Poppinga - - PowerPoint PPT Presentation

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SLAM Survey: Simultaneous Localization and Mapping Martin Poppinga - - PowerPoint PPT Presentation

MIN Faculty Department of Informatics University of Hamburg SLAM SLAM Survey: Simultaneous Localization and Mapping Martin Poppinga University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics


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SLIDE 1

University of Hamburg

MIN Faculty Department of Informatics SLAM

SLAM

Survey: Simultaneous Localization and Mapping Martin Poppinga

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems

  • 23. November 2015

Martin Poppinga 1

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SLIDE 2

University of Hamburg

MIN Faculty Department of Informatics SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 2

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SLIDE 3

University of Hamburg

MIN Faculty Department of Informatics Motivation SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 3

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SLIDE 4

University of Hamburg

MIN Faculty Department of Informatics Motivation SLAM

Mobile Robotics

◮ (Partly) autonomous Systems ◮ Many applications

◮ SAR (Search and Rescue) ◮ Exploration (Areal, Underwater, Space) ◮ Service

◮ Challenges

◮ Mapping ◮ Localization

Martin Poppinga 4

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University of Hamburg

MIN Faculty Department of Informatics Motivation SLAM

SLAM

◮ Challenge

◮ Map → Localization ◮ Localization → Map ◮ Chicken-egg problem

◮ SLAM brings this together ◮ Different approaches

◮ Filtering ◮ Sensors

Martin Poppinga 5

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University of Hamburg

MIN Faculty Department of Informatics History SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 6

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SLIDE 7

University of Hamburg

MIN Faculty Department of Informatics History SLAM

History

◮ First steps

◮ In mid 80s ◮ Mapping and localization ◮ Limited in computation power

◮ Breakthrough

◮ In mid 90s ◮ Convergence of errors ◮ Mapping and localization together ◮ Demonstration on real systems

◮ Wide interest in 2000s

Martin Poppinga 7

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 8

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SLIDE 9

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Definition

◮ Existing information

◮ robots Controls / odometry ◮ UT = {u1, u2, u3, ...uT} ◮ Observations ◮ ZT = {z1, z2, z3, ...zT}

◮ Needed information

◮ Map (with its features) ◮ m ◮ Path of the robot ◮ XT = {x0, x1, x2, ...xT}

Martin Poppinga 9

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Graphical Model

[3]

Martin Poppinga 10

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Probabilistic SLAM

◮ World is not perfect! ◮ Full SLAM

◮ p(XT, m|ZT, UT)

◮ Online SLAM

◮ p(xt, m|ZT, UT)

◮ This has to be estimated ◮ Different problems different estimators ◮ Choosing one based on the problem

Martin Poppinga 11

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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SLIDE 13

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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SLIDE 17

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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SLIDE 18

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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SLIDE 19

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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SLIDE 20

University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Different Problems

◮ Static vs dynamic ◮ Volumetric vs feature based ◮ Topologic vs metric ◮ Known vs unknown correspondence ◮ Large vs small uncertainty ◮ Active vs passive ◮ Single- vs multiagent ◮ Any time and any space ◮ ⇒ Lots of different approaches

Martin Poppinga 12

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Sensing

◮ Range

◮ Laser Range sensors ◮ Sonar ◮ Tactile ◮ ...

◮ Visual

◮ Camera ◮ 3D Camera

◮ Other

◮ Wi-fi ◮ Sound ◮ ...

Martin Poppinga 13

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Algorithm

◮ Handling the errors

◮ Location ◮ Landmark sensoring

◮ Features in m ◮ Measurement model ◮ Motion model ◮ Three main filter types ◮ Will be partly presented in IR lecture

Martin Poppinga 14

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University of Hamburg

MIN Faculty Department of Informatics Basics SLAM

Filters

◮ Kalman Filter

◮ The original technique in SLAM ◮ Reduction of errors ◮ Mathematical model

◮ Particle Filter

◮ Sequential Monte Carlo ◮ Particle for possible locations

◮ Graph Based

Martin Poppinga 15

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SLIDE 24

University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 16

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

EKF SLAM

◮ First variants of SLAM ◮ Kalman-filter based ◮ Standard kalman filter ◮ Provisional landmark list

Martin Poppinga 17

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[3]

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

FastSLAM

◮ Particle Filter ◮ Each particle one Position ◮ Rao-Blackwellization ◮ Independent features ◮ No revising of path on the fly ◮ Performance ◮ Widely used

Martin Poppinga 19

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

GraphSLAM

◮ Builds graph

◮ Movement ◮ Observations

◮ Flexible edges

Martin Poppinga 20

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

Which to Use?

◮ Depends ◮ EKF SLAM

◮ Quadratic with landmarks ◮ Big maps problematic (submaps)

◮ GraphSLAM

◮ Elegant solution ◮ Full SLAM / offline

◮ FastSLAM

◮ Data association ◮ Efficient

Martin Poppinga 21

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

DARPA

◮ US military research ◮ Self driving cars in desert ◮ GPS not precise enough ◮ Stanley, winner grand challenge 2005

Martin Poppinga 22

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University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

Project Tango

◮ Project by Google ◮ Phablet with special hardware ◮ Devkit available ◮ Targeted to consumer market

Martin Poppinga 23

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

Outline

  • 1. Motivation
  • 2. History
  • 3. Basics
  • 4. Implementations
  • 5. Conclusion

Martin Poppinga 24

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

Current & Future Research

◮ Popular on conferences ◮ Optimization

◮ Computation power ◮ Sensors ◮ Algorithms

◮ New environments

◮ Air ◮ Underwater

◮ Feature matching ◮ Loop closure

Martin Poppinga 25

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

Conclusion

◮ First productive systems ◮ Need improvement ◮ General purpose algorithm difficult ◮ Sensor quality important ◮ Frameworks & Tools

◮ ROS ◮ OpenSLAM ◮ Mobile Robot Programming Toolkit (MRPT)

Martin Poppinga 26

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

Thank you for your attention!

Martin Poppinga 27

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

References

[1] J Aulinas, YR Petillot, J Salvi, and X Llad´

  • .

The slam problem: a survey. CCIA, 2008. [2] H. Durrant-Whyte and T. Bailey. Simultaneous localization and mapping: part i. IEEE Robotics & Automation Magazine, 13(2):99–110, 6 2006. [3] B Siciliano and O Khatib. Springer handbook of robotics. 2008.

Martin Poppinga 28

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University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

The SLAM Problem

[2]

Martin Poppinga 29