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

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

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

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

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

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

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

Motivation 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 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

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.

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.

Feature Detection Detected Feature Points Superimposed feature tracks through images

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.

Robust Estimation The Monocular Scheme Separate the matching point s 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.

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

Experiments Different Platforms

Experiments Speed and Accuracy

Experiments Visual Odometry vs. Differential GPS

Experiments Visual Odometry vs. Inertial Navigation System (INS)

Experiments Visual Odometry vs. Wheel Recorder

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.