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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

Visual OdometryDavid Nister, CVPR 2004

2005. 1. 4

Computer Vision Lab.

Young Ki Baik

contents
Contents
  • Introduction
  • Algorithm
  • Experimental results
  • Conclusion and opinion.
introduction
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
introduction1
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

algorithm
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

algorithm1
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

algorithm2
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.
algorithm3

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

algorithm4
Algorithm
  • Merit of using the Stereo Vision
    • Known scale (baseline)
    • Less affection by uncertainty in depth
algorithm5

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

algorithm6
Algorithm
  • The Stereo Scheme

3D motion

estimation

Certain number of frames

Optimization (LM)

Coordinate system is transferred.

Firewall

For stopping propagation error

experimental results
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 results1
Experimental results
  • Processing time
    • Around 13Hz
  • Location error
  • Direction error
experimental results2
Experimental results
  • Performance

Red line : Visual odometry

Blue line : DGPS

experimental results3
Experimental results
  • Performance

Red : Visual odometry

Blue : DGPS

conclusion and opinion
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.