视觉里程计的经典入门教程.pdf

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Davide Scaramuzza University of Zurich Robotics and Perception Group http://rpg.ifi.uzh.ch/ Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/

 Scaramuzza, D., Fraundorfer, F., Visual Odometry: Part I - The First 30 Years and Fundamentals, IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011.  Fraundorfer, F., Scaramuzza, D., Visual Odometry: Part II - Matching, Robustness, and Applications, IEEE Robotics and Automation Magazine, Volume 19, issue 1, 2012. Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/

VO is the process of incrementally estimating the pose of the vehicle by examining the changes that motion induces on the images of its onboard cameras input output Image sequence (or video stream) from one or more cameras attached to a moving vehicle Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/ Camera trajectory (3D structure is a plus):

 Sufficient illumination in the environment  Dominance of static scene over moving objects  Enough texture to allow apparent motion to be extracted  Sufficient scene overlap between consecutive frames Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/ Is any of these scenes good for VO? Why?

 Contrary to wheel odometry, VO is not affected by wheel slip in uneven terrain or other adverse conditions.  More accurate trajectory estimates compared to wheel odometry (relative position error 0.1% − 2%)  VO can be used as a complement to  wheel odometry  GPS  inertial measurement units (IMUs)  laser odometry  In GPS-denied environments, such as underwater and aerial, VO has utmost importance Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/

Image 1 Image 2 Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/

 VO computes the camera path incrementally (pose after pose) Image sequence Feature detection Feature matching (tracking) Motion estimation 2D-2D 3D-3D 3D-2D Tk+1,k Local optimization SIFT features tracks Ck+1 Ck Ck-1 Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/ Tk,k-1

 Ck+1 Ck Tk+1,k Tk,k-1 Ck-1 Copyright of Davide Scaramuzza - davide.scaramuzza@ieee.org - https://sites.google.com/site/scarabotix/

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