Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David...
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![Page 1: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/1.jpg)
Visual Odometry for Vehicles in Urban Environments
CS223B Computer Vision, Winter 2008Team 3: David Hopkins, Christine Paulson, Justin Schauer
![Page 2: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/2.jpg)
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
![Page 3: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/3.jpg)
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
![Page 4: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/4.jpg)
Selecting reliable features is key3067 SIFT candidate features
276 feature correspondences after mutual consistency check
69 feature correspondences after RANSAC
![Page 5: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/5.jpg)
Example Trajectory Animation
Car turns left, then right onto a street with oncoming traffic
Mean Absolute Error: 6 mTotal Distance: 322 m
Link 2 3
Web 2 3
QuickTime™ and a decompressor
are needed to see this picture.
![Page 6: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/6.jpg)
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
![Page 7: Visual Odometry for Vehicles in Urban Environments CS223B Computer Vision, Winter 2008 Team 3: David Hopkins, Christine Paulson, Justin Schauer.](https://reader035.fdocuments.us/reader035/viewer/2022062320/56649d5e5503460f94a3d2ed/html5/thumbnails/7.jpg)
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)