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

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

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

◮ (Partly) autonomous Systems ◮ Many applications

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

◮ Challenges

◮ Mapping ◮ Localization

Martin Poppinga 4

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

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

◮ 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

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

◮ 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

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

◮ 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Martin Poppinga 17

[3]

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

University of Hamburg

MIN Faculty Department of Informatics Implementations SLAM

GraphSLAM

◮ Builds graph

◮ Movement ◮ Observations

◮ Flexible edges

Martin Poppinga 20

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

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

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

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

◮ Popular on conferences ◮ Optimization

◮ Computation power ◮ Sensors ◮ Algorithms

◮ New environments

◮ Air ◮ Underwater

◮ Feature matching ◮ Loop closure

Martin Poppinga 25

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

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 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

University of Hamburg

MIN Faculty Department of Informatics Conclusion SLAM

The SLAM Problem

[2]

Martin Poppinga 29