VisualOdometryDavidNisterCVPR.pptVIP

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VisualOdometryDavidNisterCVPR

Visual Odometry David Nister, CVPR 2004 2005. 1. 4 Computer Vision Lab. Young Ki Baik Contents Introduction Algorithm Experimental results Conclusion and opinion. Introduction Visual Odometry Usage of visual information as a sensor Realization of the real-time navigation system using 3D reconstruction algorithms (camera motion estimation algorithm) Features for real-time Parallel processing based PC (MMX) Pentium III 1GHz Fast algorithm Preemptive RANSAC (ICCV2003) Features for accuracy Stereo camera Calibrated framework Introduction System overview Algorithm Feature extraction Harris corner detector No subpixel precision detection Usage of down sampled data (16 bit) Size of INT and FLOAT is 32 bit. Low size of data can be expected more efficiency for parallel processing. Algorithm Feature matching Normalized correlation over an 11x11 window 11x11 = 121 (for applying to 128 bit aligned memory) Matching with converted 1 dimensional vector using Parallel processing (MMX) is faster than normal method. Short search range (Video sequences have short base line) Algorithm 3D reconstruction 5-point algorithm Only considering pose estimation. Usage of 2D points. Preemptive RANSAC (CVPR 2003) Fast RANSAC Triangulation method Conventional triangulation method is used for 3D reconstruction. Bundle adjustment Using small number of parameters and iteration. Algorithm Motion estimation 3-point algorithm Only considering camera pose (rotation and translation) estimation. Usage of 3D point. Algorithm Merit of using the Stereo Vision Known scale (baseline) Less affection by uncertainty in depth Algorithm The Stereo Scheme Algorithm The Stereo Scheme Experimental results System configuration CPU : Pentium III 1GHz (MMX) Stereo camera (360*240*2) size / FOV : 50? / Baseline : 28 cm Experiments GPS : Location error test INS : Direction error test Environment Loop Meadow Woods Experimental results Processing time Around 13Hz Location error Direction error Exp

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