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SLAM for Dummies

1 slam for Dummies A Tutorial Approach to Simultaneous Localization and Mapping By the Dummies S ren Riisgaard and Morten Rufus Blas 2 1. Table of contents 1. TABLE OF 2. INTRODUCTION ..4 3. ABOUT slam ..6 4. THE THE THE RANGE MEASUREMENT 5. THE slam PROCESS ..10 6. LASER DATA ..14 7. ODOMETRY DATA ..15 8. LANDMARKS ..16 9. LANDMARK EXTRACTION ..19 SPIKE LANDM MULTIPLE 10. DATA ASSOCIATION ..25 11. THE EKF ..28 OVERVIEW OF THE THE The system state: X ..29 The covariance matrix: The Kalman gain: The Jacobian of the measurement model: H ..31 The Jacobian of the prediction model: A ..33 The slam specific Jacobians: Jxr and The process noise: Q and The measurement noise: R and V ..35 STEP 1: UPDATE CURRENT STATE USING THE ODOMETRY STEP 2: UPDATE STATE FROM RE-OBSERVED STEP 3: ADD NEW LANDMARKS TO THE CURRENT 12.

robot. This is accomplished by extracting features from the environment and re-observing when the robot moves around. An EKF (Extended Kalman Filter) is the heart of the SLAM process. It is responsible for updating where the robot thinks it is based on these features. These features are commonly called landmarks and will be

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