1. Visual Odometry David Nister, CVPR 2004
Computer Vision Lab.
Young Ki Baik

2. Contents Introduction Algorithm Experimental results
Conclusion and opinion.

3. Introduction Visual Odometry Features for real-time
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 Feature extraction 3D reconstruction
Matching
and
tracking
Motion
estimation
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)
121 7
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. Algorithm Motion estimation R, T 3-point algorithm
Only considering camera pose (rotation and translation) estimation.
Usage of 3D point.
Generated points
R, T
Triangle
Selected points

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

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

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

12. Experimental results System configuration Experiments Environment
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 Location error Direction error
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.
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.