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Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan 1

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Outline Introduction Objective Calibration Process Our Work Improving Laser Data Automate Data Collection Conclusion 2

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LRF-Camera System 3

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LRF-Camera Calibration Problem Definition Find the transformation [R |t ] of the camera w.r.t. LRF 5

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Objective Related Work LRF-Camera Calibration Calibration of a multi-sensor system laser rangefinder/camera, 1995 More Accurate Result Extrinsic calibration of a camera and laser range finder (improves camera calibration), 2004 Easier Process An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features, 2007 6 Proposal Improving Laser Data Filtering Laser Data

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Objective 7 Proposal Improving Laser Data Filtering Laser Data Thesis Improving Laser Data On Improving Laser Data for Extrinsic LRF/Camera Calibration, 2011 Automated Process Automated Calibration Data Collection in LRF/Camera Calibration with Online Feedback, 2012 Related Work LRF-Camera Calibration Calibration of a multi-sensor system laser rangefinder/camera, 1995 More Accurate Result Extrinsic calibration of a camera and laser range finder (improves camera calibration), 2004 Easier Process An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features, 2007

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Objective Related Work LRF-Camera Calibration Calibration of a multi-sensor system laser rangefinder/camera, 1995 More Accurate Result Extrinsic calibration of a camera and laser range finder (improves camera calibration), 2004 Easier Process An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features, 2007 8 Proposal Improving Laser Data Filtering Laser Data Thesis Improving Laser Data On Improving Laser Data for Extrinsic LRF/Camera Calibration, 2011 Automated Process Automated Calibration Data Collection in LRF/Camera Calibration with Online Feedback, 2012

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Calibration Process 9 Data Collection Optimization Check Result End Start Feature Detection

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Calibration Process Data Collection 10

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Calibration Process Feature Detection 11

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Calibration Process Projection Error 12

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Calibration Process Optimization Simulated Annealing : Find global minimum Levenberg-Marquardt : Find local minimum 13

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Calibration Process Result Project laser data onto an image 14

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Our Work Improving Laser Data Automatic Data Collection 15

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Improving Laser Data Angular Error => 16

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Simulation Angular Error => 17

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Simulation Laser Data Improvement 18 Target Methodimp plai n ratioimp plai n ratio average RMS 0.3 2 0.5 4 54.8 % 0.9 2 2.0 4 48.0 % S.D. of RMS 0.0 07 0.0 32 22.1 % 0.0 03 0.0 19 16.5 %

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Experiment Laser Range Finder Camera 19

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Experiment Laser Data Improvement 20 TargetStingrayLegria Methodimp plai n ratioimp plai n ratio average RMS 1.6 6 2.9 0 57.3 % 6.1 8 11.3 8 54.3 % S.D. of RMS 0.0 2 0.0 4 40.7 % 0.1 9 0.4 8 38.8 %

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Experiment Number of Data 21

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Improving Laser Data Lower bound 22

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Simulation Lower Bound 23 RMS Rati o 1 27 0 0.5 0.5 9 0.5592.9 2 54 0 0.5 1.1 8 1.0992.9 3 27 0 1.0 1.1 8 1.0992.3 454 0 1.02.3 6 2.1892.3

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Automate Data Collection 24 Data Collection Optimization Check Result End Start Feature Detection 5 30 1 5 30 2

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Automate Data Collection Feature Detection 25

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Automate Data Collection False Detection => Tracking 26

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Experiment Data Distribution 27 0.440.0021.030.063 0.390.0030.780.037 0.500.0031.160.078 0.440.0050.690.011

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Automate Data Collection Working Space Covering Data Bin (x, y, angle) 28

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Automate Data Collection Moving Calibration Object => Velocity Metric 29

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Experiment Velocity & Accuracy 30

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Experiment Accuracy & Time 31

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Experiment 32

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Automate Data Collection User Interface Data Quality Metric Tracking, Velocity Data Distribution Data Bins, Current Bin, Target Bin 33

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Automate Data Collection User Interface Result Laser Data Projection Acknowledge & Warning Sound Data Acquire, Tracking Lost 34

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Conclusion Improved calibration method Reduce projection error to 50 percent Automatic data collection process Faster and easier for all user 35

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