SLAM for Dummies.pdf

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By the ‘dummies’

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3. ABOUT SLAM........................................................................................................................6

4. THE HARDWARE..................................................................................................................7

THE ROBOT.....................................................................................................................................7

THE RANGE MEASUREMENT DEVICE.................................................................................................8

5. THE SLAM PROCESS.........................................................................................................10

6. LASER DATA.......................................................................................................................14

7. ODOMETRY DATA.............................................................................................................15

8. LANDMARKS.......................................................................................................................16

9. LANDMARK EXTRACTION..............................................................................................19

SPIKE LANDMARKS .......................................................................................................................19

RANSAC.....................................................................................................................................20

The covariance matrix: P.........................................................................................................30

The Kalman gain: K.................................................................................................................31

The Jacobian of the measurement model: H .............................................................................31

The Jacobian of the prediction model: A ..................................................................................33

The SLAM specific Jacobians: J

xr

The process noise: Q and W.....................................................................................................35

The measurement noise: R and V .............................................................................................35

STEP 1: UPDATE CURRENT STATE USING THE ODOMETRY DATA.......................................................36

STEP 2: UPDATE STATE FROM RE-OBSERVED LANDMARKS ..............................................................37

STEP 3: ADD NEW LANDMARKS TO THE CURRENT STATE .................................................................39

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The goal of this document is to give a tutorial introduction to the field of SLAM

(Simultaneous Localization And Mapping) for mobile robots. There are numerous

papers on the subject but for someone new in the field it will require many hours of

research to understand many of the intricacies involved in implementing SLAM. The

hope is thus to present the subject in a clear and concise manner while keeping the

prerequisites required to understand the document to a minimum. It should actually

be possible to sit down and implement basic SLAM after having read this paper.

The motivation behind writing this paper is primarily to help ourselves understand

SLAM better. One will always get a better knowledge of a subject by teaching it.

Second of all most of the existing SLAM papers are very theoretic and primarily

focus on innovations in small areas of SLAM, which of course is their purpose. The

purpose of this paper is to be very practical and focus on a simple, basic SLAM

algorithm that can be used as a starting point to get to know SLAM better. For people

with some background knowledge in SLAM we here present a complete solution for

SLAM using EKF (Extended Kalman Filter). By complete we do not mean perfect.

What we mean is that we cover all the basic steps required to get an implementation

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C# and the code will compile in the .Net Framework v. 1.1. Most of the code is very

straightforward and can be read almost as pseudo-code, so porting to other languages

or platforms should be easy.