Visual Odometry for Vehicles in Urban Environments

CS223B Computer Vision, Winter 2008Team 3: David Hopkins, Christine Paulson, Justin Schauer

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

Approach: SIFT features, RANSAC, derive rotation and translation from essential matrix

1. Identify corresponding SIFT features between image pairs2. Estimate the fundamental matrix that satisfies the epipolar

constraint for uncalibrated cameras: using adaptive RANSAC to refine F and reject outliers3. 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

Selecting reliable features is key3067 SIFT candidate features

276 feature correspondences after mutual consistency check

69 feature correspondences after RANSAC

Example Trajectory Animation

Car turns left, then right onto a street with oncoming traffic

Mean Absolute Error: 6 mTotal Distance: 322 m

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Mean Absolute Error: 1 – 3 percent

Car driving backwards

Mean Absolute Error: 2.2 mTotal 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

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)