University of Hamburg

MIN Faculty

Department of Informatics

SLAM

SLAMSurvey: Simultaneous Localization and Mapping

Martin Poppinga

University of HamburgFaculty of Mathematics, Informatics and Natural SciencesDepartment of Informatics

Technical Aspects of Multimodal Systems

23. November 2015

Martin Poppinga 1

University of Hamburg

MIN Faculty

Department of Informatics

SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 2

University of Hamburg

MIN Faculty

Department of Informatics

Motivation SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 3

University of Hamburg

MIN Faculty

Department of Informatics

Motivation SLAM

Mobile Robotics

I (Partly) autonomous SystemsI Many applications

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

I ChallengesI MappingI Localization

Martin Poppinga 4

University of Hamburg

MIN Faculty

Department of Informatics

Motivation SLAM

SLAM

I ChallengeI Map → LocalizationI Localization → MapI Chicken-egg problem

I SLAM brings this togetherI Different approaches

I FilteringI Sensors

Martin Poppinga 5

University of Hamburg

MIN Faculty

Department of Informatics

History SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 6

University of Hamburg

MIN Faculty

Department of Informatics

History SLAM

History

I First stepsI In mid 80sI Mapping and localizationI Limited in computation power

I BreakthroughI In mid 90sI Convergence of errorsI Mapping and localization togetherI Demonstration on real systems

I Wide interest in 2000s

Martin Poppinga 7

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 8

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Definition

I Existing informationI robots Controls / odometry

I UT = {u1, u2, u3, ...uT}I Observations

I ZT = {z1, z2, z3, ...zT}I Needed information

I Map (with its features)I m

I Path of the robotI XT = {x0, x1, x2, ...xT}

Martin Poppinga 9

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Graphical Model

[3]

Martin Poppinga 10

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Probabilistic SLAM

I World is not perfect!I Full SLAM

I p(XT ,m|ZT ,UT )

I Online SLAMI p(xt ,m|ZT ,UT )

I This has to be estimated

I Different problems different estimators

I Choosing one based on the problem

Martin Poppinga 11

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Different Problems

I Static vs dynamic

I Volumetric vs feature based

I Topologic vs metric

I Known vs unknown correspondence

I Large vs small uncertainty

I Active vs passive

I Single- vs multiagent

I Any time and any space

I ⇒ Lots of different approaches

Martin Poppinga 12

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Sensing

I RangeI Laser Range sensorsI SonarI TactileI ...

I VisualI CameraI 3D Camera

I OtherI Wi-fiI SoundI ...

Martin Poppinga 13

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Algorithm

I Handling the errorsI LocationI Landmark sensoring

I Features in m

I Measurement model

I Motion model

I Three main filter types

I Will be partly presented in IR lecture

Martin Poppinga 14

University of Hamburg

MIN Faculty

Department of Informatics

Basics SLAM

Filters

I Kalman FilterI The original technique in SLAMI Reduction of errorsI Mathematical model

I Particle FilterI Sequential Monte CarloI Particle for possible locations

I Graph Based

Martin Poppinga 15

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 16

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

EKF SLAM

I First variants of SLAM

I Kalman-filter based

I Standard kalman filter

I Provisional landmark list

Martin Poppinga 17

[3]

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

FastSLAM

I Particle Filter

I Each particle one Position

I Rao-Blackwellization

I Independent features

I No revising of path on the fly

I Performance

I Widely used

Martin Poppinga 19

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

GraphSLAM

I Builds graphI MovementI Observations

I Flexible edges

Martin Poppinga 20

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

Which to Use?

I DependsI EKF SLAM

I Quadratic with landmarksI Big maps problematic (submaps)

I GraphSLAMI Elegant solutionI Full SLAM / offline

I FastSLAMI Data associationI Efficient

Martin Poppinga 21

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

DARPA

I US military research

I Self driving cars in desert

I GPS not precise enough

I Stanley, winner grand challenge 2005

Martin Poppinga 22

University of Hamburg

MIN Faculty

Department of Informatics

Implementations SLAM

Project Tango

I Project by Google

I Phablet with special hardware

I Devkit available

I Targeted to consumer market

Martin Poppinga 23

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

Outline

1. Motivation

2. History

3. Basics

4. Implementations

5. Conclusion

Martin Poppinga 24

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

Current & Future Research

I Popular on conferencesI Optimization

I Computation powerI SensorsI Algorithms

I New environmentsI AirI Underwater

I Feature matching

I Loop closure

Martin Poppinga 25

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

Conclusion

I First productive systems

I Need improvement

I General purpose algorithm difficult

I Sensor quality importantI Frameworks & Tools

I ROSI OpenSLAMI Mobile Robot Programming Toolkit (MRPT)

Martin Poppinga 26

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

Thank you for your attention!

Martin Poppinga 27

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

References

[1] J Aulinas, YR Petillot, J Salvi, and X Llado.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

University of Hamburg

MIN Faculty

Department of Informatics

Conclusion SLAM

The SLAM Problem

[2]

Martin Poppinga 29