3630-08-lec14-features - Introduction Detection/Estimation...

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Unformatted text preview: Introduction Detection/Estimation Sonar Features Line Features Summary References Feature Extraction Henrik I Christensen Robotics & Intelligent Machines @ GT Georgia Institute of Technology, Atlanta, GA 30332-0760 hic@cc.gatech.edu Henrik I Christensen (RIM@GT) Features 1 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Outline 1 Introduction 2 Detection/Estimation 3 Sonar Features 4 Line Features 5 Summary Henrik I Christensen (RIM@GT) Features 2 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References System Context Henrik I Christensen (RIM@GT) Features 3 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Introduction Compression of sensor data to key features How do we detect these features? How do we estimate the parameters for the features? A few examples Henrik I Christensen (RIM@GT) Features 4 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Outline 1 Introduction 2 Detection/Estimation 3 Sonar Features 4 Line Features 5 Summary Henrik I Christensen (RIM@GT) Features 5 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Point Estimation Many sensor generate range estimates (Sonar, GPS, Laser) Triangulation is a well known technique for estimation of points Fusion of multiple range readings into an estimate Theory is well known from phased array radar Henrik I Christensen (RIM@GT) Features 6 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Point Estimation r1 r1 r2 r1 r2 Henrik I Christensen (RIM@GT) Features 7 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Point Estimation Generalizes to many sensors hits x T = x s 1 + 1 d 2 s ( dxs d 2 r | dys | q r 2 2 d 2 s- d 4 r ) y T = y s 1 + 1 d 2 s ( dys d 2 r | dxs | q r 2 2 d 2 s- d 4 r ) The intersection point has no general solution so it becomes a minimization problem. Henrik I Christensen (RIM@GT) Features 8 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Line Estimation Lines are a predominant feature in engineered environments There is an abundance of methods for line estimation LSQ, Split-Merge, Hough, EM-estimation, RANSAC is frequently used (Fischler & Bolles, 1981) Henrik I Christensen (RIM@GT) Features 9 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References Voting based estimators Voting provides a simple estimator for detection Voting requires: 1 A Voting Space 2 A voting function (structure function) 3 A decision function (often local extrema) Hough (1962) is one of the most widely used. Can also be used for lines and other shapes (Ballard, 1981) Henrik I Christensen (RIM@GT) Features 10 / 38 Introduction Detection/Estimation Sonar Features Line Features Summary References The Hough Transform x y Line model: = x * cos(...
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This note was uploaded on 01/27/2011 for the course CS 3803 taught by Professor Staff during the Spring '10 term at Georgia Institute of Technology.

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3630-08-lec14-features - Introduction Detection/Estimation...

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