l3_intro_slam

l3_intro_slam - Introduction to SLAM Simultaneous...

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Introduction to SLAM Simultaneous Localization And Mapping Paul Robertson Cognitive Robotics Wed Feb 9 th , 2005
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Outline • Introduction • Localization •SLAM • Kalman Filter –Examp le • Large SLAM – Scaling to large maps
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Introduction 3 • (Localization) Robot needs to estimate its location with respects to objects in its environment (Map provided). • (Mapping) Robot need to map the positions of objects that it encounters in its environment (Robot position known) • (SLAM) Robot simultaneously maps objects that it encounters and determines its position (as well as the position of the objects) using noisy sensors.
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Localization • Tracking – Bounded uncertainty – Can flip into kidnapping problem • Global Localization – Initially huge uncertainties – Degenerates to tracking • Kidnapping Problem – Unexpected global localization Tracking Global Localization Kidnapped 5
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Representing Robots Position of robot is represented as a triple consisting of its x t , y t components and its heading θ t : X t =(x t , y t , θ t ) T X t+1 =X t + (u t t cos θ t , u t t sin θ t , u t t ) T x t x t+1 θ t x i , y i u t 6
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Kalman Filter Localization Robot Landmark 7
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Kalman Filter Localization Robot Landmark 8
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Kalman Filter Localization Robot Landmark 9
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Kalman Filter Localization Robot Landmark 10
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Basic SLAM • Localize using a Kalman Filter (EKF) • Consider all landmarks as well as the robot position as part of the posterior. • Use a single state vector to store estimates
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This note was uploaded on 11/07/2011 for the course AERO 16.410 taught by Professor Brianwilliams during the Fall '05 term at MIT.

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l3_intro_slam - Introduction to SLAM Simultaneous...

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