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griesbach - Sensor Fusion Systems Overview and Mathematics...

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Sensor Fusion Systems Overview and Mathematics Bjoern Griesbach [email protected]
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Sensor Fusion Systems [Content] 1. Introduction 1. Motivation 2. Different tracking options 2. Existing Multi Sensor Fusion Systems 1. Fusion of head mounted and fixed sensor data 2. Fusion of magnetic & optical sensor data 3. Fusion of gyroscope & optical sensor data 4. Open Tracker – an open source AR software 3. Mathematics of Sensor Fusion 1. Kalman Filter 2. Particle Filter
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Motivation Each sensor has its strengths and weaknesses One sensor is never sufficient for reliable tracking Optimal tracking = use multiple sensors Sensor Data Fusion Component Precise Estimation Sensor Sensor Noisy Data Noisy Data Noisy Data
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Motivation Augmented Reality Virtual Reality Mobile Robots Multi Sensor Fusion used in various fields of research:
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Different Tracking Options Magnetic Tracking reliable stable fast Optical Tracking precise time consuming Gyroscope precise drift error Fixed Trackers no limit in size, weight highly precise (e.g. stereo vision) in tracking objects bad for head orientation Mobile Trackers (i.e. Head Mounted Tracker) good for head orientation limited in size & weight less precise in tracking objects Technology Location
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Content 1. Introduction 1. Motivation 2. Different tracking options 2. Existing Multi Sensor Fusion Systems 1. Fusion of head mounted and fixed sensor data 2. Fusion of magnetic & optical sensor data 3. Fusion of gyroscope & optical sensor data 4. Open Tracker – an open source AR software 3. Mathematics of Sensor Fusion 1. Kalman Filter 2. Particle Filter
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Fusion of Data from Head Mounted and Fixed Sensors Two optical trackers : Mobile (Head Mounted) Fixed Wanted: Fusing data of fixed and mobile tracker: Hybrid inside-out & outside-in approach in order to Estimate pose of a certain object (for example a head’s pose) How to realize?
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Fusion of Data from Head-Mounted and Fixed Sensors Pose of an object is represented as a vector =(x,y,z,α,β,γ); By transforming poses of two different sensors into the same coordinate system, one will get two (noisy) measurements and for the same object Each measurement is weighted differently depends on the variance of the measurement Each measurement has its own variance σ i 2 represented by a matrix P i x 1 z 2 z i z i z
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Fusion of Data from Head-Mounted and Fixed Sensors Given: pose measurements: z 1 z 2 with covariance matrices P 1 P 2 from two different sensors Wanted: optimal weights to get optimal estimate Solution: Optimal estimate x with minimal combined covariance matrix P : ; ; 1 2 1 2 2 2 1 1 1 2 1 2 P P P P P z P P P z P P P x + = + + + = Already a simple form of the Kalman Filter - Remember!
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Fusion of Data from Head-Mounted and Fixed Sensors Experiment by W. Hoff, First International Workshop on AR San Francisco Result : max. translational error was reduced by 90%
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Content 1. Introduction 1.
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