Visual Odometry for Ground Vehicle Applications

1. CPSC 643, Presentation 4 Visual Odometry for Ground Vehicle Applications David Nister, Oleg Naroditsky, James Bergen Sarnoff Corporation, CN5300 Princeton, NJ 08530 Journal of Field Robotics. Vol.23, No.1, pp. 3-200, 2006.

2. Mostly Related Works • Stereo Visual Odometry – Agrawal 2007 • Monocular Visual Odometry – Campbell 2005 • A Visual Odometry System– Olson 2003 • Previous Work of this Paper – Nister 2004

3. Mostly Related Works • Stereo Visual Odometry– Agrawal 2007 • Monocular Visual Odometry – Campbell 2005 • A Visual Odometry System– Olson 2003 • Previous Work of this Paper – Nister 2004 • Integrate with IMU/GPS • Bundle Adjustment

4. Mostly Related Works • Stereo Visual Odometry – Agrawal 2007 • Monocular Visual Odometry – Campbell 2005 • A Visual Odometry System– Olson 2003 • Previous Work of this Paper – Nister 2004 • 3-DOF Camera • Optical Flow Method

5. Mostly Related Works • Stereo Visual Odometry – Agrawal 2007 • Monocular Visual Odometry – Campbell 2005 • A Visual Odometry System– Olson 2003 • Previous Work of this Paper – Nister 2004 • With absolute orientation sensor • Forstner interest operator in the left Image, matches from left to right • Use approximate prior knowledge • Iteratively select landmark points

6. Mostly Related Works • Stereo Visual Odometry – Agrawal 2007 • Monocular Visual Odometry – Campbell 2005 • A Visual Odometry System– Olson 2003 • Previous Work of this Paper – Nister 2004 • Estimates ego-motion using a hand-held Camera • Real-time algorithm based on RANSIC

7. Other Related Works • Simultaneous Localization and Mapping (SLAM) • Robot Navigation with Omnidirectional Camera

8. Motivation • Nister: • Use pure visual information • Use Harris corner detection in all • images, track feature to feature • No prior knowledge • RANSIC based estimation in real- • time • Olson: • With absolute orientation sensor • Forstner interest operator in the • left Image, matches from left to • right • Use approximate prior knowledge • Iteratively select landmark points

9. Feature Detection • Harris Corner Detection • Search for the local maxima of the corner strength . • determinant, trance, constant, window area, • derivatives of input image, weight function.

10. Feature Detection • Four Sweeps to Calculate • Compute , by filters and . • Calculate the horizontal sum by filter . • Calculate the vertical sum by filter . • Calculate corner strength .

11. Feature Detection • Detected Feature Points • Superimposed feature tracks through images

12. Feature Matching • Two Directional Matching • Calculate the normalized correlation in reign, where , are two consecutive input images. • Match the feature points in the circular area that have the maximum correlation in two directions.

13. Robust Estimation • The Monocular Scheme • Separate the matching points into 5-points groups. • Treat each group as a 5-poi-nt relative pose problem. • Use RANSAC to select well matched groups. • Estimate camera motion us-ing the selected groups. • Put the current estimation in to the coordinate of the prev-ious one.

14. Robust Estimation • The Stereo Scheme • Match the feature points in stereo images, then triangulatethem into 3D points. • Estimation the camera motion using RANSAC and the 3D points in consecutive frames.

15. Experiments Different Platforms

16. Experiments Speed and Accuracy

17. Experiments Visual Odometry vs. Differential GPS

18. Experiments Visual Odometry vs. Inertial Navigation System (INS)

19. Experiments Visual Odometry vs. Wheel Recorder

20. Conclusion and Future Work • Conclusion • A real-time ego motion estimation system. • Work both on monocular camera and stereo head. • Results are accurate and robust. • Future Work • Integrate visual odometry with Kalman filter. • Use sampling methods with multimodal distributions.