Visual Odometry

1. Visual Odometry Chris Moore Mark Huetsch Firouzeh Jalilian

2. Problem: Estimate camera motion • Estimate trajectory of a vehicle which captured sequence of images from all four sides

3. Approach • Compute SIFT features for a stream of frames from a camera • Set a window size for the number of consecutive images to handle at a time • For each window of n frames • For first 2 frames • Find corresponding SIFT features (rough correspondences) • Calculate essential matrix using RANSAC to discard inaccurate correspondences • Decompose essential matrix into rotation and translation, and calculate sparse 3D structure (Can only find translation and structure calculate up to a scale!) • For later frames i=3…n • Find rough SIFT correspondences between frame i and frame 1 • Calculate motion from frame 1 to frame i using 3D structure computed in a • Use sparse bundle adjustment on all frames in window to refine camera motions and calculate scale • Advance the window by 1 frame, repeat step 3

4. Result: SIFT matches • SIFT features matched and inliers identified using RANSAC

5. Improvements • Gaussian convolution of produced data-points • Self-tuning of RANSAC parameters • More intelligent sliding window for bundle adjustment

6. Hidden Slide • Chris 40% • Mark 30% • Firouzeh 30%