1. Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer

2. Goal: Determine Vehicle Trajectory from Video Cameras Mounted on a Vehicle 2 calibrated cameras: forward-looking & side-looking with non-overlapping field of view Compare visual odometry results to GPS and inertial sensor ground-truth data

3. Approach:SIFT features, RANSAC, derive rotation and translation from essential matrix 1. Identify corresponding SIFT features between image pairs 2. Estimate the fundamental matrix that satisfies the epipolar constraint for uncalibrated cameras: using adaptive RANSAC to refine F and reject outliers 3. Compute the essential matrix from the fundamental matrix and the camera calibration matrix: 4. Recover rotation and translation components from the essential matrix using singular value decomposition (SVD) 4 solutions: Pick one where world points are in front of both cameras

4. Selecting reliable features is key 3067 SIFT candidate features 276 feature correspondences after mutual consistency check 69 feature correspondences after RANSAC

5. Example Trajectory Animation Car turns left, then right onto a street with oncoming traffic Mean Absolute Error: 6 m Total Distance: 322 m LinkLink 2 323 WebWeb 2 323

6. Mean Absolute Error: 1 – 3 percent Car driving backwards Mean Absolute Error: 2.2 m Total Distance: 141 m Straight road with lots of traffic Mean Absolute Error: 2.7m Total Distance: 312 m Mean Absolute Error: 0.3 m Total Distance: 27 m Mean Absolute Error: 0.6 m Total Distance: 23 m Mean Absolute Error: 1.7 m Total Distance: 90 m

7. Conclusions / Issues Cumulative error is extremely sensitive to orientation Adaptive RANSAC was helpful in reducing effects of moving vehicles Visual odometry is not a replacement for GPS, but could be used as an alternate or complementary method to GPS (i.e. tunnels, parking structures, Mars rovers)

8. Equal Team Efforts