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Visual Odometry David Nister, CVPR 2004

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

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Visual Odometry David Nister, CVPR 2004

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  1. Visual OdometryDavid Nister, CVPR 2004 2005. 1. 4 Computer Vision Lab. Young Ki Baik

  2. Contents • Introduction • Algorithm • Experimental results • Conclusion and opinion.

  3. 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

  4. Introduction • System overview 3D reconstruction Feature extraction Motion estimation Matching and tracking 5-point algorithm / P-RANSAC / Triangulation method / Bundle adjustment Harris corner detector Normalized correlation 3-point algorithm for 3D motion

  5. 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. 32 bit MMX register 16 bit 64bit

  6. 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) 7 121 Garbage space … Matching using MMX … …

  7. 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.

  8. R, T Algorithm • Motion estimation • 3-point algorithm • Only considering camera pose (rotation and translation) estimation. • Usage of 3D point. Generated points Triangle Selected points

  9. Algorithm • Merit of using the Stereo Vision • Known scale (baseline) • Less affection by uncertainty in depth

  10. 3D motion (3-P algo., P-RANSAC) Motion estimation (5-P algo., P-RANSAC) Triangulation Stereo camera Matching Algorithm • The Stereo Scheme Triangulation Stereo camera Matching Next frame R, T

  11. Algorithm • The Stereo Scheme 3D motion estimation Certain number of frames Optimization (LM) Coordinate system is transferred. Firewall For stopping propagation error

  12. 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

  13. Experimental results • Processing time • Around 13Hz • Location error • Direction error

  14. Experimental results • Performance Red line : Visual odometry Blue line : DGPS

  15. Experimental results • Performance Red : Visual odometry Blue : DGPS

  16. Conclusion and Opinion. • Conclusion • Real-time navigation system is implemented. • Opinion • There is no refinement scheme for solving closing loop problem. • More fast result with Pentium-IV (SSE2) • There is room for improvement.

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