1. On the Analysis of the Depth Error on the Road Plane for Monocular Vision-Based Robot Navigation
Dezhen Song
CSE Dept., Texas A&M Univ.
Hyunnam Lee
Samsung Techwin Robot Business
Jingang Yi
MAE Dept., Rutgers Univ.

2. INTRODUCTION
Camera
Explain scenario.

3. Structure from Motion (SfM)
Hartley and Zisserman, 2003; Huang and Netravali, 1994; Aggarwal and Nandhakumar, 1988
Baseline

4. Imperfect World

5. Reconstruction Uncertainty

6. Depth Error Couples with Motion!
In a conventional approach, robot makes its motion to the second view position and take the image. Then it perform 3D reconstruction to obtain obstacle distribution.
However, if motion is made and the second camera center is fixed! There will no control of depth uncertainty!
The robot could hit the obstacle because 3D reconstruction quality cannot be guaranteed!
To decouple the two , we need a model that can predict depth error uncertain as a function of potential second view configurations. Therefore, it will become possible to actively reduce the depth uncertainty even before the actual 3D reconstruction.

7. Problem Definition Given threshold |eΔ| for depth uncertainty,
predict un-trusted area Au where the depth error is beyond the threshold.
In a conventional approach, robot makes its motion to the second view position and take the image. Then it perform 3D reconstruction to obtain obstacle distribution.
However, if motion is made and the second camera center is fixed! There will no control of depth uncertainty!
The robot could hit the obstacle because 3D reconstruction quality cannot be guaranteed!
To decouple the two , we need a model that can predict depth error uncertain as a function of potential second view configurations. Therefore, it will become possible to actively reduce the depth uncertainty even before the actual 3D reconstruction.

8. Related Work: Vision-Based Navigation
Visual-based Navigation
No Hands Across America (1995)
The ARGO project(1998)
Autonomous Motorcycle (2007)
Visual SLAM and Visual Odometry
Mouragnon et al. 2006
Chen et al. 2006
Mortard et al. 2007; Lemaire and Lacroix 2007
Hayet et al. 2008

9. Structure from Motion Reduce correspondence error Other features
Low-rank approximation, Tomasi and Kanade, 1992
Power factorization, Hartley and Schaffalizky, 2003
Closure constraint, Guilbert and Bartoli, 2003
Covariance weighted data, Anandan and Irani, 2002
Other features
Planar parallax ,Irani et al., 2002, Bartoli and Sturm, 2003
Probability of correspondence points,
Dellaert et al. 2000

10. Related Work: Active Vision
Intelligent data acquisition process
Survey
Bajscy, 1988
Aloimonos et al. 1987, 1990
Maximizing camera visibility
Li and Liu, 2005
Marchand and Chaumette, 1999
Minimizing 3D estimation error
Chaumette et.al, 1996
Dunn and Olague, 2005
Bakhatari and benhabib,

11. Related Work: Scene Construction Error Analysis
Estimation error
Covariance matrix
Matthies and Shafer, 1987, Sun and Ramesh, 2001
Standard deviation of depth
Kim et. Al, 2005
Low boundary
Young and Chellappa, 1992, Daniilidis and Nagel, 1993
Sensitivity map
Xiong and Matthies, 1997

12. Assumptions Static or slow-moving obstacles Known camera parameters
Uniform pixel correspondence error
Iso-oriented cameras

13. Pin-hole Camera Model

14. Computing Depth

15. Computing Depth

16. Depth Error Range eΔ eΔ

17. Depth Error Range

18. Predicting Untrusted Area Au

19. Predicting Untrusted Area Au

20. Experiments
The robot and the camera used in experiments.

21. Depth Error Range eΔ

22. Untrusted Area Au

23. Experiments: Application in Depth-Error-Aware Motion Planning
(20cm, 0cm)
(-6cm, 0cm) 2 3 1
(13cm, -50cm)
(-20cm, -50cm)

24. Experiments Relative depth error (22) (28) (33) (33) (27)
Image resolution

25. Experiments
(46)
(33)
(26)
Depth of the obstacles

26. Conclusion and Future Work
Choose camera perspectives
Mixed initiative planning
Visual landmark selection in visual odometry
Improve visual tracking performance for mobile robots
In a conventional approach, robot makes its motion to the second view position and take the image. Then it perform 3D reconstruction to obtain obstacle distribution.
However, if motion is made and the second camera center is fixed! There will no control of depth uncertainty!
The robot could hit the obstacle because 3D reconstruction quality cannot be guaranteed!
To decouple the two , we need a model that can predict depth error uncertain as a function of potential second view configurations. Therefore, it will become possible to actively reduce the depth uncertainty even before the actual 3D reconstruction.