Visual Odometry for Ground Vehicle Applications David Nistér, Oleg Naroditsky, and James Bergen Sarnoff Corporation CN5300 Princeton, New Jersey 08530.

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Presentation transcript:

Visual Odometry for Ground Vehicle Applications David Nistér, Oleg Naroditsky, and James Bergen Sarnoff Corporation CN5300 Princeton, New Jersey 08530

Basic idea  Estimation of motion based on video input alone  No prior knowledge of scene or motion  Real time operation with low delay  Front end: feature tracker  Point features matched between pairs of frames  Can be used in conjunction with other sensors

Introduction  Effective use of video sensors has been a goal for many years  Recent advances have made real-time vision processing practical  Speed and latency constraints

Related works  Moravec’s work in 1970  Stereo visual odometry used on Mars early 2004  Camera motion estimation based on feature tracks  More closely related to Davison; Chiuso,Favaro & Soatto

Results obtained using this system Left: Single camera Right: Stereo pair

Feature Detection

Finding corner strength

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.

Selecting corner points

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 points into 5-points groups. Treat each group as a 5-point relative pose problem. Use RANSAC to select well matched groups. Estimate camera motion using the selected groups. Put the current estimation in to the coordinate of the previ- ous 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.